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0
|
Can you inform me who are the authors of "Patient-Specific Physiological Monitoring And Prediction Using Structured Gaussian Processes?"?
|
Yang Yang, Chris Pugh, Kate Niehaus, Clare MacEwen, Glen W. Colopy, D Clifton, Tingting Zhu
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you inform me who are the authors of \"Patient-Specific Physiological Monitoring And Prediction Using Structured Gaussian Processes?\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Yang Yang, Chris Pugh, Kate Niehaus, Clare MacEwen, Glen W. Colopy, D Clifton, Tingting Zhu"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 0,
"split": "train"
}
|
1
|
Can you inform me who are the authors of "Long Range Optical Truck Tracking.?"?
|
Dietrich Paulus, Christian Winkens
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you inform me who are the authors of \"Long Range Optical Truck Tracking.?\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Dietrich Paulus, Christian Winkens"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 1,
"split": "train"
}
|
2
|
Can you inform me who are the authors of "0.33 mm 2 13.3 dB gain sub-6 GHz to 28 GHz transformer-coupled low-voltage upconversion mixer for 5G applications?"?
|
Oner Hanay, Renato Negra
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you inform me who are the authors of \"0.33 mm 2 13.3 dB gain sub-6 GHz to 28 GHz transformer-coupled low-voltage upconversion mixer for 5G applications?\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Oner Hanay, Renato Negra"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 2,
"split": "train"
}
|
3
|
Could you tell me the authors of the paper "Physical parameter extraction over urban areas using L-band POLSAR data and interferometric baseline diversity?"?
|
Stefan Sauer, Eric Pottier, Laurent Ferro-Famil, Andreas Reigber
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCould you tell me the authors of the paper \"Physical parameter extraction over urban areas using L-band POLSAR data and interferometric baseline diversity?\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Stefan Sauer, Eric Pottier, Laurent Ferro-Famil, Andreas Reigber"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 3,
"split": "train"
}
|
4
|
Who are the authors of paper "A New Node Deployment and Location Dispatch Algorithm for Underwater Sensor Networks.?"?
|
Lurong Jiang, Peng Jiang, Binfeng Ruan, Jun Liu, Feng Wu
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho are the authors of paper \"A New Node Deployment and Location Dispatch Algorithm for Underwater Sensor Networks.?\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Lurong Jiang, Peng Jiang, Binfeng Ruan, Jun Liu, Feng Wu"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 4,
"split": "train"
}
|
5
|
Who are the authors of paper "Distributed global development parametric cost modeling?"?
|
Raymond J. Madachy
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho are the authors of paper \"Distributed global development parametric cost modeling?\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Raymond J. Madachy"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 5,
"split": "train"
}
|
6
|
Could you tell me the authors of the paper "BlockHammer: Improving Flash Reliability by Exploiting Process Variation Aware Proactive Failure Prediction?"?
|
Fei Wu, Wenmin Zhong, Qiulin Wu, Changsheng Xie, Zhonghai Lu, Ruixiang Ma, Jiguang Wan
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCould you tell me the authors of the paper \"BlockHammer: Improving Flash Reliability by Exploiting Process Variation Aware Proactive Failure Prediction?\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Fei Wu, Wenmin Zhong, Qiulin Wu, Changsheng Xie, Zhonghai Lu, Ruixiang Ma, Jiguang Wan"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 6,
"split": "train"
}
|
7
|
Can you tell me who wrote the paper "Biologically Inspired Neural Controller For Robot Learning And Mapping?"?
|
Alejandra Barrera Ramirez, Alfredo Weitzenfeld Ridel
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me who wrote the paper \"Biologically Inspired Neural Controller For Robot Learning And Mapping?\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Alejandra Barrera Ramirez, Alfredo Weitzenfeld Ridel"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 7,
"split": "train"
}
|
8
|
Can you tell me who wrote the paper "Sub-Bottom Sediment Classification Using Reliable Instantaneous Frequency Calculation and Relaxation Time Estimation?"?
|
Hongmei Zhang, Jianhu Zhao, Shaobo Li, Siheng Qu
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me who wrote the paper \"Sub-Bottom Sediment Classification Using Reliable Instantaneous Frequency Calculation and Relaxation Time Estimation?\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Hongmei Zhang, Jianhu Zhao, Shaobo Li, Siheng Qu"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 8,
"split": "train"
}
|
9
|
Could you tell me the authors of the paper "Robust assembly line balancing with heterogeneous workers?"?
|
Jean-François Cordeau, Gilbert Laporte, Mayron César O. Moreira, Alysson M. Costa
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCould you tell me the authors of the paper \"Robust assembly line balancing with heterogeneous workers?\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Jean-François Cordeau, Gilbert Laporte, Mayron César O. Moreira, Alysson M. Costa"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 9,
"split": "train"
}
|
10
|
What organization is researcher Justin Booth-Clibborn affiliated with?
|
New York, New York
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhat organization is researcher Justin Booth-Clibborn affiliated with?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "New York, New York"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 10,
"split": "train"
}
|
11
|
What organization is researcher Greg Daville affiliated with?
|
Hove East Sussex, United Kingdom
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhat organization is researcher Greg Daville affiliated with?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Hove East Sussex, United Kingdom"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 11,
"split": "train"
}
|
12
|
Which organization does researcher Kohta Morie belong to?
|
Transistor Studio, Japan
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhich organization does researcher Kohta Morie belong to?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Transistor Studio, Japan"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 12,
"split": "train"
}
|
13
|
Can you tell me the affiliation of the researcher Coke Ferreiro?
|
Spain
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the affiliation of the researcher Coke Ferreiro?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Spain"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 13,
"split": "train"
}
|
14
|
Can you tell me the affiliation of the researcher C. A. Petri?
|
GMD Bonn Germany Germany
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the affiliation of the researcher C. A. Petri?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "GMD Bonn Germany Germany"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 14,
"split": "train"
}
|
15
|
Can you tell me the affiliation of the researcher Nikita Deshpande?
|
DSK Supinfocom, Pune
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the affiliation of the researcher Nikita Deshpande?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "DSK Supinfocom, Pune"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 15,
"split": "train"
}
|
16
|
Which institution is the researcher Franz Von Kutschera associated with?
|
Philosophische FakultÄt I UniversitÄt Regensburg UniversitÄtstrasse 31 Postfach 397 8400 Regensburg Germany
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhich institution is the researcher Franz Von Kutschera associated with?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Philosophische FakultÄt I UniversitÄt Regensburg UniversitÄtstrasse 31 Postfach 397 8400 Regensburg Germany"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 16,
"split": "train"
}
|
17
|
Can you tell me the affiliation of the researcher Marcin Kobylecki?
|
Warszawa, Poland
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the affiliation of the researcher Marcin Kobylecki?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Warszawa, Poland"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 17,
"split": "train"
}
|
18
|
Which organization does researcher Theo D’Hondt belong to?
|
Vrije Universiteit Brussel Belgium
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhich organization does researcher Theo D’Hondt belong to?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Vrije Universiteit Brussel Belgium"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 18,
"split": "train"
}
|
19
|
Which institution is the researcher Arthur Lemmens associated with?
|
Amsterdam
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhich institution is the researcher Arthur Lemmens associated with?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Amsterdam"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 19,
"split": "train"
}
|
20
|
Where is the paper "Scalable Lossless And Lossy Image Coding Based On The Rwhat Plus P Pyramid And The Inter-Coefficient Classification Method" published?
|
2008 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-4
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhere is the paper \"Scalable Lossless And Lossy Image Coding Based On The Rwhat Plus P Pyramid And The Inter-Coefficient Classification Method\" published?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "2008 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-4"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 20,
"split": "train"
}
|
21
|
In which place was the paper "A method for sub-sample computation of time displacements between discrete signals based only on discrete correlation sequences." published?
|
Biomedical Signal Processing and Control
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nIn which place was the paper \"A method for sub-sample computation of time displacements between discrete signals based only on discrete correlation sequences.\" published?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Biomedical Signal Processing and Control"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 21,
"split": "train"
}
|
22
|
What is the publication location for the paper "Private sector needs for trusted/secure computer systems"?
|
AFIPS National Computer Conference
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhat is the publication location for the paper \"Private sector needs for trusted/secure computer systems\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "AFIPS National Computer Conference"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 22,
"split": "train"
}
|
23
|
In which place was the paper "Design Of Ring Oscillator Structures For Measuring Isolated Nbti And Pbti" published?
|
2012 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS 2012)
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nIn which place was the paper \"Design Of Ring Oscillator Structures For Measuring Isolated Nbti And Pbti\" published?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "2012 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS 2012)"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 23,
"split": "train"
}
|
24
|
In which place was the paper "Cooperative Aerial Manipulation with Decentralized Adaptive Force-Consensus Control" published?
|
Journal of Intelligent & Robotic Systems
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nIn which place was the paper \"Cooperative Aerial Manipulation with Decentralized Adaptive Force-Consensus Control\" published?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Journal of Intelligent & Robotic Systems"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 24,
"split": "train"
}
|
25
|
Where has the paper "On the evaluation of the Eigenvalues of a banded toeplitz block matrix" been published?
|
Journal of Complexity
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhere has the paper \"On the evaluation of the Eigenvalues of a banded toeplitz block matrix\" been published?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Journal of Complexity"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 25,
"split": "train"
}
|
26
|
Where has the paper "Lbp Channels For Pedestrian Detection" been published?
|
2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018)
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhere has the paper \"Lbp Channels For Pedestrian Detection\" been published?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018)"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 26,
"split": "train"
}
|
27
|
Where is the paper "Calculation of Generalized Polynomial-Chaos Basis Functions and Gauss Quadrature Rules in Hierarchical Uncertainty Quantification" published?
|
Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhere is the paper \"Calculation of Generalized Polynomial-Chaos Basis Functions and Gauss Quadrature Rules in Hierarchical Uncertainty Quantification\" published?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions "
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 27,
"split": "train"
}
|
28
|
Can you tell me the publication venue of the paper "Effects Of Wind Estimation Errors On Ocean Surface Current Retrieval For A Doppler Scatterometer"?
|
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the publication venue of the paper \"Effects Of Wind Estimation Errors On Ocean Surface Current Retrieval For A Doppler Scatterometer\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 28,
"split": "train"
}
|
29
|
Where is the paper "High Performance Parallel Optical Flow Algorithms on the Sony Playstation 3" published?
|
VMV
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhere is the paper \"High Performance Parallel Optical Flow Algorithms on the Sony Playstation 3\" published?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "VMV"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 29,
"split": "train"
}
|
30
|
Who teamed up with author Martin Kozek at Vienna Univ Technol, Inst Mech & Mechatron, Div Control & Proc Automat, A-1040 Vienna, Austria to write the paper "Estimation of molecular weight distribution of CIS-polyisoprene melts from dielectric loss spectra using neural networks"?
|
Denis Miklau, Jorgl, H.P.
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho teamed up with author Martin Kozek at Vienna Univ Technol, Inst Mech & Mechatron, Div Control & Proc Automat, A-1040 Vienna, Austria to write the paper \"Estimation of molecular weight distribution of CIS-polyisoprene melts from dielectric loss spectra using neural networks\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Denis Miklau, Jorgl, H.P."
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 30,
"split": "train"
}
|
31
|
Who teamed up with author ignatio madanhire at Univ Johannesburg, Dept Qual & Operat Management, Johannesburg, South Africa to write the paper "Cleaner production as a tool to mitigate pollution in leather processing: Case study"?
|
Charles Mbohwa
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho teamed up with author ignatio madanhire at Univ Johannesburg, Dept Qual & Operat Management, Johannesburg, South Africa to write the paper \"Cleaner production as a tool to mitigate pollution in leather processing: Case study\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Charles Mbohwa"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 31,
"split": "train"
}
|
32
|
Who collaborate with author A. V. Simonova in National Research Tomsk Polytechnic University, Russia to write paper "The fast neutron irradiation influence on the AlGaAs IR-LEDs reliability."?
|
A. V. Gradoboev, K. N. Orlova, I. A. Asanov
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho collaborate with author A. V. Simonova in National Research Tomsk Polytechnic University, Russia to write paper \"The fast neutron irradiation influence on the AlGaAs IR-LEDs reliability.\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "A. V. Gradoboev, K. N. Orlova, I. A. Asanov"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 32,
"split": "train"
}
|
33
|
Who joined forces with author Bao N Nguyen at Univ Leeds, Sch Chem, Inst Proc Res & Dev, Leeds LS2 9JT, W Yorkshire, England to write the research paper "Predicting Solvent-Dependent Nucleophilicity Parameter with a Causal Structure Property Relationship"?
|
Samuel Boobier, Yufeng Liu, Krishna Sharma, A. J. Blacker, David R J Hose, Nikil Kapur
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho joined forces with author Bao N Nguyen at Univ Leeds, Sch Chem, Inst Proc Res & Dev, Leeds LS2 9JT, W Yorkshire, England to write the research paper \"Predicting Solvent-Dependent Nucleophilicity Parameter with a Causal Structure Property Relationship\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Samuel Boobier, Yufeng Liu, Krishna Sharma, A. J. Blacker, David R J Hose, Nikil Kapur"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 33,
"split": "train"
}
|
34
|
Who joined forces with author Vijay Varadharajan at University of Western Sydney School of Computing and IT 10 Kingswood NSW 2747 Australia to write the research paper "A logic for state transformations in authorization policies"?
|
Yun Bai
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho joined forces with author Vijay Varadharajan at University of Western Sydney School of Computing and IT 10 Kingswood NSW 2747 Australia to write the research paper \"A logic for state transformations in authorization policies\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Yun Bai"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 34,
"split": "train"
}
|
35
|
Who teamed up with author José A. Castellanos at Univ Zaragoza, Dept Informat & Ingn Sistemas, C Maria Luna 3, E-50015 Zaragoza, Spain to write the paper "A fast and accurate approximation for planar pose graph optimization"?
|
Basilio Bona, Luca Carlone, Rosario Aragues
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho teamed up with author José A. Castellanos at Univ Zaragoza, Dept Informat & Ingn Sistemas, C Maria Luna 3, E-50015 Zaragoza, Spain to write the paper \"A fast and accurate approximation for planar pose graph optimization\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Basilio Bona, Luca Carlone, Rosario Aragues"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 35,
"split": "train"
}
|
36
|
Who teamed up with author Ken Yocum at Department of Computer Science|Duke University to write the paper "Grid allocation and reservation - Improving grid resource allocation via integrated selection and binding"?
|
Yang-Suk Kee, Henri Casanova, Andrew A. Chien
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho teamed up with author Ken Yocum at Department of Computer Science|Duke University to write the paper \"Grid allocation and reservation - Improving grid resource allocation via integrated selection and binding\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Yang-Suk Kee, Henri Casanova, Andrew A. Chien"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 36,
"split": "train"
}
|
37
|
Who collaborate with author Alexey Stupnikov in Queens Univ Belfast, Computat Biol & Machine Learning Lab, Ctr Canc Res & Cell Biol, Sch Med Dent & Biomed Sci,Fac Med Hlth & Life Sci, Belfast BT9 7AE, Antrim, North Ireland to write paper "samExploreR: exploring reproducibility and robustness of RNA-seq results based on SAM files."?
|
Darragh G. McArt, Ricardo de Matos Simoes, Galina V. Glazko, Shailesh Tripathi, Matthias Dehmer, Frank Emmert-streib, Manuel Salto-Tellez
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho collaborate with author Alexey Stupnikov in Queens Univ Belfast, Computat Biol & Machine Learning Lab, Ctr Canc Res & Cell Biol, Sch Med Dent & Biomed Sci,Fac Med Hlth & Life Sci, Belfast BT9 7AE, Antrim, North Ireland to write paper \"samExploreR: exploring reproducibility and robustness of RNA-seq results based on SAM files.\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Darragh G. McArt, Ricardo de Matos Simoes, Galina V. Glazko, Shailesh Tripathi, Matthias Dehmer, Frank Emmert-streib, Manuel Salto-Tellez"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 37,
"split": "train"
}
|
38
|
Who worked with Antonio Torralba from Massachusetts Institute of Technology on the paper titled "Unbiased look at dataset bias"?
|
Alexei A. Efros
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho worked with Antonio Torralba from Massachusetts Institute of Technology on the paper titled \"Unbiased look at dataset bias\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Alexei A. Efros"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 38,
"split": "train"
}
|
39
|
Who collaborate with author Zhongxuan Luo in Dalian Univ Technol, Sch Math Sci, Dalian 116621, Peoples R China to write paper "A High-Capacity 3D Steganography Algorithm With Adjustable Distortion."?
|
RiMing Sun, Jiangbei Hu, Nannan Li, Shengfa Wang
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho collaborate with author Zhongxuan Luo in Dalian Univ Technol, Sch Math Sci, Dalian 116621, Peoples R China to write paper \"A High-Capacity 3D Steganography Algorithm With Adjustable Distortion.\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "RiMing Sun, Jiangbei Hu, Nannan Li, Shengfa Wang"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 39,
"split": "train"
}
|
40
|
Who is the author of both "Trust computation for multiple routes recommendation in social network sites." and "A Sentiment Delivering Estimate Scheme Based on Trust Chain in Mobile Social Network." papers?
|
Zhenhua Huang
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho is the author of both \"Trust computation for multiple routes recommendation in social network sites.\" and \"A Sentiment Delivering Estimate Scheme Based on Trust Chain in Mobile Social Network.\" papers?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Zhenhua Huang"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 40,
"split": "train"
}
|
41
|
Who writed both the paper "Performance of Electric Power Systems Under Physical Malicious Attacks." and paper "Critical Infrastructures: The Need For International Risk Governance"?
|
Wolfgang Kröger
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho writed both the paper \"Performance of Electric Power Systems Under Physical Malicious Attacks.\" and paper \"Critical Infrastructures: The Need For International Risk Governance\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Wolfgang Kröger"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 41,
"split": "train"
}
|
42
|
Who is the author of both "Emergent Behavior in Swarms with On-board Sensing." and "Secure robotics." papers?
|
Daniel M. Lofaro
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho is the author of both \"Emergent Behavior in Swarms with On-board Sensing.\" and \"Secure robotics.\" papers?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Daniel M. Lofaro"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 42,
"split": "train"
}
|
43
|
Who is the author of both "A design theory for systems that support emergent knowledge processes" and "Introduction To The 2004 Ais Award Papers On Innovation In Information Systems Education" papers?
|
M.Lynne Markus
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho is the author of both \"A design theory for systems that support emergent knowledge processes\" and \"Introduction To The 2004 Ais Award Papers On Innovation In Information Systems Education\" papers?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "M.Lynne Markus"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 43,
"split": "train"
}
|
44
|
Who penned both "Reflecting on social influence in networks" and "Liquid Democracy: An Analysis in Binary Aggregation and Diffusion."?
|
Zoé Christoff
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho penned both \"Reflecting on social influence in networks\" and \"Liquid Democracy: An Analysis in Binary Aggregation and Diffusion.\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Zoé Christoff"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 44,
"split": "train"
}
|
45
|
Who has written both the "Efficient Hybrid Continuous-Time/Discrete-Time ∑ Δ Modulators for Broadband Wireless Telecom Systems." and "Undersampling RF-to-digital CT ΣΔ modulator with tunable notch frequency and simplified raised-cosine FIR feedback DAC." papers?
|
José Manuel de la Rosa
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho has written both the \"Efficient Hybrid Continuous-Time/Discrete-Time ∑ Δ Modulators for Broadband Wireless Telecom Systems.\" and \"Undersampling RF-to-digital CT ΣΔ modulator with tunable notch frequency and simplified raised-cosine FIR feedback DAC.\" papers?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "José Manuel de la Rosa"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 45,
"split": "train"
}
|
46
|
Who has written both the "Multicast communication in wormhole-routed star graph interconnection networks" and "Performance of adaptive multistage interference canceller for multi-carrier CDMA systems" papers?
|
Nen-Chung Wang
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho has written both the \"Multicast communication in wormhole-routed star graph interconnection networks\" and \"Performance of adaptive multistage interference canceller for multi-carrier CDMA systems\" papers?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Nen-Chung Wang"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 46,
"split": "train"
}
|
47
|
Who penned both "Dismantling the Environmental Context - the Role of Environmental Characteristics in the Organizational Adoption of Open Standard-Based Inter-Organizational Information Systems." and "Maternal Identity and Maternal Role Attainment - Determinants of mothers' Participation in Maternal Virtual Communities."?
|
Julia Krönung
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho penned both \"Dismantling the Environmental Context - the Role of Environmental Characteristics in the Organizational Adoption of Open Standard-Based Inter-Organizational Information Systems.\" and \"Maternal Identity and Maternal Role Attainment - Determinants of mothers' Participation in Maternal Virtual Communities.\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Julia Krönung"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 47,
"split": "train"
}
|
48
|
Who writed both the paper "Model-based coupling approach for non-iterative real-time co-simulation" and paper "Discrete Time Sliding Mode Control Strategies For Buffered Networked Systems"?
|
Martin Horn
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho writed both the paper \"Model-based coupling approach for non-iterative real-time co-simulation\" and paper \"Discrete Time Sliding Mode Control Strategies For Buffered Networked Systems\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Martin Horn"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 48,
"split": "train"
}
|
49
|
Who penned both "Real-time connected-word recognition in a noisy environment" and "A 10000-word continuous-speech recognition system"?
|
h piotrowski
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho penned both \"Real-time connected-word recognition in a noisy environment\" and \"A 10000-word continuous-speech recognition system\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "h piotrowski"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 49,
"split": "train"
}
|
50
|
Who within Dept. of Electr. & Comput. Eng., Univ. of Rochester, Rochester, NY, USA|c| has the highest number of collaborations with author Renatas Jakushokas?
|
Eby G. Friedman
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho within Dept. of Electr. & Comput. Eng., Univ. of Rochester, Rochester, NY, USA|c| has the highest number of collaborations with author Renatas Jakushokas?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Eby G. Friedman"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 50,
"split": "train"
}
|
51
|
Who is the closest collaborator with author Igor Moreira in Brazilian Telecommun Agcy, Enforcement Superintendency, Brasilia, DF, Brazil? Closeness is defined in terms of the number of collaboration together.?
|
Cecilio Pimentel
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho is the closest collaborator with author Igor Moreira in Brazilian Telecommun Agcy, Enforcement Superintendency, Brasilia, DF, Brazil? Closeness is defined in terms of the number of collaboration together.?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Cecilio Pimentel"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 51,
"split": "train"
}
|
52
|
Who is the closest collaborator with author Abhishek Kumar Verma in Univ Calif Merced, Dept Mech Engn, Merced, CA 95343 USA? Closeness is defined in terms of the number of collaboration together.?
|
Ayyaswamy Venkattraman
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho is the closest collaborator with author Abhishek Kumar Verma in Univ Calif Merced, Dept Mech Engn, Merced, CA 95343 USA? Closeness is defined in terms of the number of collaboration together.?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Ayyaswamy Venkattraman"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 52,
"split": "train"
}
|
53
|
Who within HEBREW UNIV JERUSALEM,INST MATH & COMP SCI,IL-9104 JERUSALEM,ISRAEL has the highest number of collaborations with author Shimon Cohen?
|
nathan intrator
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho within HEBREW UNIV JERUSALEM,INST MATH & COMP SCI,IL-9104 JERUSALEM,ISRAEL has the highest number of collaborations with author Shimon Cohen?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "nathan intrator"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 53,
"split": "train"
}
|
54
|
Who within Northwestern Polytech Univ, Coll Automat, Xian 710129, Shaanxi, Peoples R China has the highest number of collaborations with author Lei Yu?
|
Bingchang Zhou
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho within Northwestern Polytech Univ, Coll Automat, Xian 710129, Shaanxi, Peoples R China has the highest number of collaborations with author Lei Yu?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Bingchang Zhou"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 54,
"split": "train"
}
|
55
|
Who is the closest collaborator with author Bruce J. Schachter in Univ. of Maryland, College Park, MD? Closeness is defined in terms of the number of collaboration together.?
|
Azriel Rosenfeld
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho is the closest collaborator with author Bruce J. Schachter in Univ. of Maryland, College Park, MD? Closeness is defined in terms of the number of collaboration together.?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Azriel Rosenfeld"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 55,
"split": "train"
}
|
56
|
Who is the closest collaborator with author Chenhao Pei in Fuzhou Univ, Coll Phys & Informat Engn, Fuzhou, Peoples R China? Closeness is defined in terms of the number of collaboration together.?
|
Liqin Huang
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho is the closest collaborator with author Chenhao Pei in Fuzhou Univ, Coll Phys & Informat Engn, Fuzhou, Peoples R China? Closeness is defined in terms of the number of collaboration together.?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Liqin Huang"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 56,
"split": "train"
}
|
57
|
Who within Tufts Univ, Medford, MA 02155 USA has the highest number of collaborations with author Fumeng Yang?
|
Steven Franconeri
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho within Tufts Univ, Medford, MA 02155 USA has the highest number of collaborations with author Fumeng Yang?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Steven Franconeri"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 57,
"split": "train"
}
|
58
|
Who is the closest collaborator with author Rubem Toledo Bergamo in Dept. of Telecommun. Eng., Fed. Inst. of Educ., Florianopolis, Brazil|c|? Closeness is defined in terms of the number of collaboration together.?
|
Paulo Cardieri
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho is the closest collaborator with author Rubem Toledo Bergamo in Dept. of Telecommun. Eng., Fed. Inst. of Educ., Florianopolis, Brazil|c|? Closeness is defined in terms of the number of collaboration together.?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "Paulo Cardieri"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 58,
"split": "train"
}
|
59
|
Who within Instituto Politécnico Nacional, ESIME Azcapotzalco, Sección de Estudios de Posgrado e Investigación, Av. de las Granjas no.682, Col. Santa Catarina, Delegación Azcapotz ... has the highest number of collaborations with author Jaime Pacheco?
|
José De Jesús Rubio
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWho within Instituto Politécnico Nacional, ESIME Azcapotzalco, Sección de Estudios de Posgrado e Investigación, Av. de las Granjas no.682, Col. Santa Catarina, Delegación Azcapotz ... has the highest number of collaborations with author Jaime Pacheco?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "José De Jesús Rubio"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 59,
"split": "train"
}
|
60
|
How many collaborators does author Christian Koidl in Salzburg Univ, Dept Comp Sci, A-5020 Salzburg, Austria have in 2011?
|
2
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nHow many collaborators does author Christian Koidl in Salzburg Univ, Dept Comp Sci, A-5020 Salzburg, Austria have in 2011?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "2"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 60,
"split": "train"
}
|
61
|
What is the number of people that Ekkapong Promin from Management of Information Technology collaborated with in 2019?
|
1
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhat is the number of people that Ekkapong Promin from Management of Information Technology collaborated with in 2019?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "1"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 61,
"split": "train"
}
|
62
|
How many collaborators does author Songyan Yang in School of Biological Science and Medical Engineering, Beihang University, Beijing, China have in 2011?
|
7
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nHow many collaborators does author Songyan Yang in School of Biological Science and Medical Engineering, Beihang University, Beijing, China have in 2011?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "7"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 62,
"split": "train"
}
|
63
|
Can you tell me the number of collaborators of author Yi Chen from Beihang Univ, State Key Lab Virtual Real Technol & Syst, Sch Comp Sci & Engn, Beijing, Peoples R China in the year 2013?
|
3
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the number of collaborators of author Yi Chen from Beihang Univ, State Key Lab Virtual Real Technol & Syst, Sch Comp Sci & Engn, Beijing, Peoples R China in the year 2013?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "3"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 63,
"split": "train"
}
|
64
|
Can you tell me the number of collaborators of author Xu Wu from Department of Computer Science, Xi'an Institute of Posts and Telecommunications, Xi'an, China in the year 2012?
|
0
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the number of collaborators of author Xu Wu from Department of Computer Science, Xi'an Institute of Posts and Telecommunications, Xi'an, China in the year 2012?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "0"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 64,
"split": "train"
}
|
65
|
Can you tell me the number of collaborators of author Alexei D. Sadovnikov from Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont., Canada|c| in the year 1993?
|
1
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the number of collaborators of author Alexei D. Sadovnikov from Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont., Canada|c| in the year 1993?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "1"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 65,
"split": "train"
}
|
66
|
Can you tell me the number of collaborators of author Hyeyun Jeong from Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, South Korea in the year 2021?
|
4
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the number of collaborators of author Hyeyun Jeong from Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, South Korea in the year 2021?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "4"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 66,
"split": "train"
}
|
67
|
In the year 1985, how many people collaborated with author Charles Bigelow at Stanford University?
|
10
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nIn the year 1985, how many people collaborated with author Charles Bigelow at Stanford University?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "10"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 67,
"split": "train"
}
|
68
|
In the year 2018, how many people collaborated with author Byunghee Moon at Inha Univ, Elect Engn Dept, Incheon 22212, South Korea?
|
3
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nIn the year 2018, how many people collaborated with author Byunghee Moon at Inha Univ, Elect Engn Dept, Incheon 22212, South Korea?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "3"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 68,
"split": "train"
}
|
69
|
What is the number of people that A. Yonezawa from Graduate Sch. of Inf. Sci. & Technol., Tokyo Univ., Japan collaborated with in 2005?
|
1
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhat is the number of people that A. Yonezawa from Graduate Sch. of Inf. Sci. & Technol., Tokyo Univ., Japan collaborated with in 2005?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "1"
},
"style": "rule"
}
|
{
"difficulty": "medium",
"domain": "dblp",
"index": 69,
"split": "train"
}
|
70
|
What is the number of papers that David Martin from CICtourGUNE, Asociac Ctr Invest Cooperat Turismo, Mikeletegi Pasealekua 56, Donostia San Sebastian, Spain and Emilio Torres-Manzanera from Univ Oviedo, Dept Stat, Gijon 33003, Spain co-authored?
|
2
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhat is the number of papers that David Martin from CICtourGUNE, Asociac Ctr Invest Cooperat Turismo, Mikeletegi Pasealekua 56, Donostia San Sebastian, Spain and Emilio Torres-Manzanera from Univ Oviedo, Dept Stat, Gijon 33003, Spain co-authored?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "2"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 70,
"split": "train"
}
|
71
|
Do you know the number of collective papers Anran Yang at Natl Univ Def Technol, Coll Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China and Ning Jing at Natl Univ Def Technol, Coll Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China have produced?
|
2
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nDo you know the number of collective papers Anran Yang at Natl Univ Def Technol, Coll Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China and Ning Jing at Natl Univ Def Technol, Coll Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China have produced?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "2"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 71,
"split": "train"
}
|
72
|
Can you tell me the count of papers written jointly by Asmaa Halbouni at Int Islamic Univ Malaysia, Dept Elect & Comp Engn, Kuala Lumpur 53100, Malaysia and Murad Halbouni at Arab Amer Univ, Dept Nat Engn & Technol Sci, Jenin 240, Palestine?
|
2
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the count of papers written jointly by Asmaa Halbouni at Int Islamic Univ Malaysia, Dept Elect & Comp Engn, Kuala Lumpur 53100, Malaysia and Murad Halbouni at Arab Amer Univ, Dept Nat Engn & Technol Sci, Jenin 240, Palestine?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "2"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 72,
"split": "train"
}
|
73
|
Can you tell me the count of papers written jointly by Kamini Sabu at Department of Electrical Engineering, Indian Institute of Technology Bombay, India and Rao, Preeti at Indian Inst Technol, Dept Elect Engn, Mumbai 400076, Maharashtra, India?
|
4
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the count of papers written jointly by Kamini Sabu at Department of Electrical Engineering, Indian Institute of Technology Bombay, India and Rao, Preeti at Indian Inst Technol, Dept Elect Engn, Mumbai 400076, Maharashtra, India?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "4"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 73,
"split": "train"
}
|
74
|
Do you know the number of collective papers Gerben S. Van der Vegt at Department of Human Resource Management and Organizational Behavior, University of Groningen, 9700 AV Groningen, The Netherlands and J. Stuart Bunderson at John M. Olin School of Business, Washington University in St. Louis, St. Louis, Missouri 63130 have produced?
|
2
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nDo you know the number of collective papers Gerben S. Van der Vegt at Department of Human Resource Management and Organizational Behavior, University of Groningen, 9700 AV Groningen, The Netherlands and J. Stuart Bunderson at John M. Olin School of Business, Washington University in St. Louis, St. Louis, Missouri 63130 have produced?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "2"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 74,
"split": "train"
}
|
75
|
What is the number of papers that Lance A. Miller from IBM Watson Research Center, Yorktown Heights, NY and Karen Jensen from IBM Thomas J. Watson Research Center, Yorktown Heights, NY co-authored?
|
2
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhat is the number of papers that Lance A. Miller from IBM Watson Research Center, Yorktown Heights, NY and Karen Jensen from IBM Thomas J. Watson Research Center, Yorktown Heights, NY co-authored?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "2"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 75,
"split": "train"
}
|
76
|
What is the number of papers that Michael Trcka from vienna university of economics and business and Martin Natter from Department of Industrial Information Processing, University of Economics, A-1200 Vienna, Austria co-authored?
|
2
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhat is the number of papers that Michael Trcka from vienna university of economics and business and Martin Natter from Department of Industrial Information Processing, University of Economics, A-1200 Vienna, Austria co-authored?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "2"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 76,
"split": "train"
}
|
77
|
Do you know the number of collective papers Lukas Graf at Augsburg Univ, Inst Math, Augsburg, Germany and Tobias Harks at Univ Augsburg, Inst Math, D-86135 Augsburg, Germany have produced?
|
2
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nDo you know the number of collective papers Lukas Graf at Augsburg Univ, Inst Math, Augsburg, Germany and Tobias Harks at Univ Augsburg, Inst Math, D-86135 Augsburg, Germany have produced?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "2"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 77,
"split": "train"
}
|
78
|
Can you tell me the count of papers written jointly by B. Y. Qu at Nanyang Technological University, School of Electrical and Electronics Engineering, Singapore and Yongsheng Zhu at Theory of Lubrication and Bearing Institute, Xi’an Jiaotong University, Xi’an, China 710049?
|
2
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the count of papers written jointly by B. Y. Qu at Nanyang Technological University, School of Electrical and Electronics Engineering, Singapore and Yongsheng Zhu at Theory of Lubrication and Bearing Institute, Xi’an Jiaotong University, Xi’an, China 710049?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "2"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 78,
"split": "train"
}
|
79
|
What is the number of papers that Jinwang Zheng from Southeast University, Nanjing, China and Weiwei Ni from Department of Computer Science and Engineering, Southeast University, Nanjing 210096, PR China co-authored?
|
4
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhat is the number of papers that Jinwang Zheng from Southeast University, Nanjing, China and Weiwei Ni from Department of Computer Science and Engineering, Southeast University, Nanjing 210096, PR China co-authored?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "4"
},
"style": "rule"
}
|
{
"difficulty": "hard",
"domain": "dblp",
"index": 79,
"split": "train"
}
|
80
|
How many academic works referenced the paper "Model of experts for decision support in the diagnosis of leukemia patients"?
|
31
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nHow many academic works referenced the paper \"Model of experts for decision support in the diagnosis of leukemia patients\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "31"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 80,
"split": "train"
}
|
81
|
Can you tell me the count of papers that cite "Automated Aspect Recommendation through Clustering-Based Fan-in Analysis"?
|
12
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the count of papers that cite \"Automated Aspect Recommendation through Clustering-Based Fan-in Analysis\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "12"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 81,
"split": "train"
}
|
82
|
Can you tell me the count of papers that cite "Parsimonious translation models for information retrieval"?
|
3
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the count of papers that cite \"Parsimonious translation models for information retrieval\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "3"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 82,
"split": "train"
}
|
83
|
Can you tell me the count of papers that cite "A View from the Bottom: Nanometer Technology AC Parametric Failures " Why, Where, and How to Detect"?
|
10
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nCan you tell me the count of papers that cite \"A View from the Bottom: Nanometer Technology AC Parametric Failures \" Why, Where, and How to Detect\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "10"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 83,
"split": "train"
}
|
84
|
How many academic works referenced the paper "Cryptology and Network Security, 7th International Conference, CANS 2008, Hong-Kong, China, December 2-4, 2008. Proceedings"?
|
22
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nHow many academic works referenced the paper \"Cryptology and Network Security, 7th International Conference, CANS 2008, Hong-Kong, China, December 2-4, 2008. Proceedings\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "22"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 84,
"split": "train"
}
|
85
|
How many papers cite the paper "Topology-aware reliability optimization for multiprocessor systems"?
|
1
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nHow many papers cite the paper \"Topology-aware reliability optimization for multiprocessor systems\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "1"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 85,
"split": "train"
}
|
86
|
What is the number of publications that cite the paper "Optimal Kinodynamic Motion Planning for 2D Reconfiguration of Self-Reconfigurable Robots"?
|
5
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhat is the number of publications that cite the paper \"Optimal Kinodynamic Motion Planning for 2D Reconfiguration of Self-Reconfigurable Robots\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "5"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 86,
"split": "train"
}
|
87
|
How many papers cite the paper "The DVB multimedia home platform (MHP) and related specifications"?
|
16
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nHow many papers cite the paper \"The DVB multimedia home platform (MHP) and related specifications\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "16"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 87,
"split": "train"
}
|
88
|
How many papers cite the paper "A Scoping Review Of Transfer Learning Research On Medical Image Analysis Using Imagenet"?
|
1
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nHow many papers cite the paper \"A Scoping Review Of Transfer Learning Research On Medical Image Analysis Using Imagenet\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "1"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 88,
"split": "train"
}
|
89
|
How many papers cite the paper "Linear colour contouring for fine art printmaking"?
|
1
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nHow many papers cite the paper \"Linear colour contouring for fine art printmaking\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "1"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 89,
"split": "train"
}
|
90
|
What is the number of papers cited by the paper "Generation, testing and recommendation of teaching materials using classification learning"?
|
7
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhat is the number of papers cited by the paper \"Generation, testing and recommendation of teaching materials using classification learning\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "7"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 90,
"split": "train"
}
|
91
|
How many citations does the paper "Geometric Detection Of Chaos By Using A Delayed Lorenz Map" have?
|
1
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nHow many citations does the paper \"Geometric Detection Of Chaos By Using A Delayed Lorenz Map\" have?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "1"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 91,
"split": "train"
}
|
92
|
How many citations does the paper "An OPS5 implementation of qualitative reasoning about physical systems" have?
|
7
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nHow many citations does the paper \"An OPS5 implementation of qualitative reasoning about physical systems\" have?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "7"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 92,
"split": "train"
}
|
93
|
What is the number of papers cited by the paper "The roofline model for oceanic climate applications"?
|
4
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhat is the number of papers cited by the paper \"The roofline model for oceanic climate applications\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "4"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 93,
"split": "train"
}
|
94
|
What is the number of papers cited by the paper "Optimal Free Binary Decision Diagrams for Computation of EARn"?
|
7
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhat is the number of papers cited by the paper \"Optimal Free Binary Decision Diagrams for Computation of EARn\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "7"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 94,
"split": "train"
}
|
95
|
How many papers does paper "Efficient Affine 2D-image Registration Using Evolutionary Strategies" cite?
|
2
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nHow many papers does paper \"Efficient Affine 2D-image Registration Using Evolutionary Strategies\" cite?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "2"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 95,
"split": "train"
}
|
96
|
How many papers does paper "Chinese OOV translation and post-translation query expansion in chinese--english cross-lingual information retrieval" cite?
|
25
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nHow many papers does paper \"Chinese OOV translation and post-translation query expansion in chinese--english cross-lingual information retrieval\" cite?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "25"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 96,
"split": "train"
}
|
97
|
How many papers does paper "Multi-objective multi-population biased random-key genetic algorithm for the 3-D container loading problem" cite?
|
19
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nHow many papers does paper \"Multi-objective multi-population biased random-key genetic algorithm for the 3-D container loading problem\" cite?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "19"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 97,
"split": "train"
}
|
98
|
What is the number of papers cited by the paper "Simulation optimization applied to injection molding"?
|
9
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nWhat is the number of papers cited by the paper \"Simulation optimization applied to injection molding\"?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "9"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 98,
"split": "train"
}
|
99
|
How many papers does paper "Ball packings with high chromatic numbers from strongly regular graphs" cite?
|
8
|
grbench
|
[
{
"content": "Solve a question answering task by repeating bundled steps that contain reasoning (<think>...</think>) followed by exactly one graph interaction (<graph>...</graph>). After each <graph> call, the environment returns feedback inside <information>...</information>. You may take as many steps as necessary.\n\nOutput protocol (bundled steps):\n- Intermediate step (must include BOTH in a single output, in this order):\n <think>...</think><graph>Function[...]</graph>\n (Then the environment responds with <information>...</information>.)\n- Final step (no more graph calls):\n <think>...</think><answer>...</answer>\n \nAvailable graph interaction functions (use exactly these names and signatures INSIDE <graph>...):\n- RetrieveNode[keyword] # retrieves the related node from the graph according to the query\n- NodeFeature[Node, feature] # returns detailed attribute information of Node for the given \"feature\" key\n- NodeDegree[Node, neighbor_type] # returns the number of \"neighbor_type\" neighbors of Node\n- NeighbourCheck[Node, neighbor_type] # lists the \"neighbor_type\" neighbors of Node and returns them\n\nRules and format:\n1) You MUST conduct reasoning inside <think>...</think> before every graph call and after every <information> you receive.\n2) Inside <graph>...</graph>, issue EXACTLY ONE function from the list below per step. Do NOT include any other text in <graph>.\n3) Do NOT fabricate <information>; it is ONLY produced by the environment immediately after your <graph> step.\n4) Keep thoughts concise and ONLY inside <think>. Do NOT put a graph call inside <think>, and do NOT put thoughts inside <graph>.\n5) The final output MUST contain ONLY one <answer>...</answer> block with the requested node main features (e.g., names), not node IDs.\n\nHere are some examples:\nDefinition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What organization is researcher Greg Daville from?\n <think>The question is asking about the organization of a researcher named Greg Daville. We need to find the node corresponding to Greg Daville in the graph.</think><graph>RetrieveNode[Greg Daville]</graph>\n <information>The ID of this retrieval target node is 53f460a7dabfaee4dc83702a.</information>\n <think>The question is asking for the organization of a researcher, so we need to check the node's organization feature from the graph.</think><graph>NodeFeature[53f460a7dabfaee4dc83702a, organization]</graph>\n <information>Hove East Sussex, United Kingdom </information>\n <think>The organization of researcher Greg Daville is Hove East Sussex, United Kingdom.</think><answer>Hove East Sussex, United Kingdom</answer> \n \n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: How many papers are written by author Nicholas Lydon?\n <think>The question is asking for the number of written papers of a specific author (Nicholas Lydon). We need to find the author node in the graph.</think><graph>RetrieveNode[Nicholas Lydon]</graph>\n <information>The ID of this retrieval target node is 53f438c3dabfaedf43596117.</information>\n <think>The question is asking for the number of papers written by Nicholas Lydon. We need to calculate the \"paper\" neighbor degree of this node.</think><graph>NodeDegree[53f438c3dabfaedf43596117, paper]</graph>\n <information>2</information>\n <think>The number of the paper neighbors is 2</think><answer>2</answer> \n\n Definition of the graph: There are three types of nodes in the graph: paper, author and venue.\n Paper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\n Paper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n Question: What was the publish venue of the paper Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars?\n <think>The question is asking information of a node (Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars). We need to find the node in the graph.</think><graph>RetrieveNode[Mass Accretion Rates in Self-Regulated Disks of T Tauri Stars]</graph>\n <information>The ID of this node is 2090642949.</information>\n <think>The question is asking the published venue of a paper, we need to check the node's venue neighbor from the graph.</think><graph>NeighbourCheck[2090642949, venue]</graph>\n <information>['1980519', '1053242']</information>\n <think>The ID of the published venue are 1980519 and 1053242. We need to get their names.</think><graph>NodeFeature[1980519, name]</graph>\n <information>the astrophysical journal</information>\n <think>We also need the name for 1053242.</think><graph>NodeFeature[1053242, name]</graph>\n <information>the atmosphere journal</information>\n <think>The name of the published venues are the astrophysical journal and the atmosphere journal</think><answer>the astrophysical journal, the atmosphere journal</answer>\n \n(END OF EXAMPLES)\n\nDefinition of the graph:\nThere are three types of nodes in the graph: paper, author and venue.\nPaper nodes have features: title, abstract, keywords, lang, and year. Author nodes have features: name and organization. Venue nodes have features: name.\nPaper nodes are linked to their author nodes, venue nodes, reference nodes (the papers this paper cite) and cited_by nodes (other papers which cite this paper). Author nodes are linked to their paper nodes. Venue nodes are linked to their paper nodes.\n\nQuestion:\nHow many papers does paper \"Ball packings with high chromatic numbers from strongly regular graphs\" cite?\n",
"role": "user"
}
] |
fact-reasoning
|
{
"ground_truth": {
"target": "8"
},
"style": "rule"
}
|
{
"difficulty": "easy",
"domain": "dblp",
"index": 99,
"split": "train"
}
|
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