Triple
T2602342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1979 NBA Finals |
E58372
|
entity |
| Predicate | MVP |
P2630
|
FINISHED |
| Object |
Tom Burleson
Tom Burleson is a retired American professional basketball center best known for his shot-blocking and rebounding in the NBA during the 1970s.
|
E282224
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Tom Burleson | Statement: [1979 NBA Finals, MVP, Tom Burleson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Burleson Context triple: [1979 NBA Finals, MVP, Tom Burleson]
-
A.
Sam Bowden
Sam Bowden is the small-town lawyer protagonist in the thriller "Cape Fear," whose family is terrorized by a vengeful ex-convict he once helped imprison.
-
B.
Rob Mullens
Rob Mullens is a college athletics administrator best known for serving as the athletic director at the University of Oregon.
-
C.
Alan Osbiston
Alan Osbiston was a British film editor known for his work on notable mid-20th-century films, including major war and drama productions.
-
D.
Edward Burleson
Edward Burleson was a prominent Texian military and political leader of the Texas Revolution who later served as vice president of the Republic of Texas.
-
E.
Dale Hunter
Dale Hunter is a former Canadian NHL center known for his gritty, physical play and leadership, most notably with the Washington Capitals.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tom Burleson Triple: [1979 NBA Finals, MVP, Tom Burleson]
Generated description
Tom Burleson is a retired American professional basketball center best known for his shot-blocking and rebounding in the NBA during the 1970s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tom Burleson Target entity description: Tom Burleson is a retired American professional basketball center best known for his shot-blocking and rebounding in the NBA during the 1970s.
-
A.
Sam Bowden
Sam Bowden is the small-town lawyer protagonist in the thriller "Cape Fear," whose family is terrorized by a vengeful ex-convict he once helped imprison.
-
B.
Rob Mullens
Rob Mullens is a college athletics administrator best known for serving as the athletic director at the University of Oregon.
-
C.
Alan Osbiston
Alan Osbiston was a British film editor known for his work on notable mid-20th-century films, including major war and drama productions.
-
D.
Edward Burleson
Edward Burleson was a prominent Texian military and political leader of the Texas Revolution who later served as vice president of the Republic of Texas.
-
E.
Dale Hunter
Dale Hunter is a former Canadian NHL center known for his gritty, physical play and leadership, most notably with the Washington Capitals.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ab4ac14040819098b13f4a27d5c8ff |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd459ca6c81908505be96d097b739 |
completed | March 7, 2026, 7:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af83d37de081909467f8caa17ce3a9 |
completed | March 10, 2026, 2:37 a.m. |
| NEDg | Description generation | batch_69af8501adc4819092035d7e55524fc8 |
completed | March 10, 2026, 2:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69af85a6060c8190a80d5633d1b8a9d5 |
completed | March 10, 2026, 2:44 a.m. |
Created at: March 6, 2026, 9:49 p.m.