data_source stringclasses 1
value | prompt stringlengths 988 128k | ability stringclasses 1
value | reward_model dict | extra_info dict |
|---|---|---|---|---|
dbpedia50 | "\n You are given a directed graph as two CSV-like sections in this order:\n\n 1) Node table ((...TRUNCATED) | graph_path | {
"style": "rule"
} | {"entities":["1000_Smiling_Knuckles","Anglo-Burmese_people"],"ground_truth":[["1000_Smiling_Knuckles(...TRUNCATED) |
dbpedia50 | "\n You are given a directed graph as two CSV-like sections in this order:\n\n 1) Node table ((...TRUNCATED) | graph_path | {
"style": "rule"
} | {"entities":["100_(30_Rock)","Frank_Westheimer"],"ground_truth":[["100_(30_Rock)","guest","Rachel_Dr(...TRUNCATED) |
dbpedia50 | "\n You are given a directed graph as two CSV-like sections in this order:\n\n 1) Node table ((...TRUNCATED) | graph_path | {
"style": "rule"
} | {"entities":["12_Monkeys","The_Killer_That_Stalked_New_York"],"ground_truth":[["12_Monkeys","distrib(...TRUNCATED) |
dbpedia50 | "\n You are given a directed graph as two CSV-like sections in this order:\n\n 1) Node table ((...TRUNCATED) | graph_path | {
"style": "rule"
} | {"entities":["17_Days_(song)","Vanity_6"],"ground_truth":[["17_Days_(song)","writer","Doctor_Fink"],(...TRUNCATED) |
dbpedia50 | "\n You are given a directed graph as two CSV-like sections in this order:\n\n 1) Node table ((...TRUNCATED) | graph_path | {
"style": "rule"
} | {"entities":["17th_Combat_Service_Support_Brigade_(Australia)","BBC"],"ground_truth":[["17th_Combat_(...TRUNCATED) |
dbpedia50 | "\n You are given a directed graph as two CSV-like sections in this order:\n\n 1) Node table ((...TRUNCATED) | graph_path | {
"style": "rule"
} | {"entities":["1932_Ford","Modena"],"ground_truth":[["1932_Ford","bodyStyle","Convertible"],["Convert(...TRUNCATED) |
dbpedia50 | "\n You are given a directed graph as two CSV-like sections in this order:\n\n 1) Node table ((...TRUNCATED) | graph_path | {
"style": "rule"
} | {"entities":["1968_United_States_Grand_Prix","1976_Dutch_Grand_Prix"],"ground_truth":[["1968_United_(...TRUNCATED) |
dbpedia50 | "\n You are given a directed graph as two CSV-like sections in this order:\n\n 1) Node table ((...TRUNCATED) | graph_path | {
"style": "rule"
} | {"entities":["1968_United_States_Grand_Prix","Mario_Andretti"],"ground_truth":[["1968_United_States_(...TRUNCATED) |
dbpedia50 | "\n You are given a directed graph as two CSV-like sections in this order:\n\n 1) Node table ((...TRUNCATED) | graph_path | {
"style": "rule"
} | {"entities":["1973_Spanish_Grand_Prix","Roman_Hryhorchuk"],"ground_truth":[["1973_Spanish_Grand_Prix(...TRUNCATED) |
dbpedia50 | "\n You are given a directed graph as two CSV-like sections in this order:\n\n 1) Node table ((...TRUNCATED) | graph_path | {
"style": "rule"
} | {"entities":["1976_Dutch_Grand_Prix","Mario_Andretti"],"ground_truth":[["1976_Dutch_Grand_Prix","thi(...TRUNCATED) |
End of preview. Expand in Data Studio
DBpedia50 (GNN subgraph in prompt)
Same schema and non-graph fields as tangyx/DBpedia50_2entities.
The Graph: section in prompt is rebuilt from the named subgraph in
gnn/data/dbpedia50/{train,test}.json (ReaRev export), not the original HF demo graph.
Build locally:
python dbpedia50/export_hf_prompts_gnn_subgraph.py \
--out_dir dbpedia50/parquet \
--gnn_data_dir gnn/data/dbpedia50
extra_info["valid_edges"] is replaced with the same named triples as the new graph; other
extra_info fields match the base dataset.
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