Triple
T1001154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Indiana Pacers (ABA) |
E21604
|
entity |
| Predicate | headCoach |
P256
|
FINISHED |
| Object |
Bobby Leonard
Bobby Leonard was a Hall of Fame basketball coach and former player best known for leading the Indiana Pacers to multiple ABA championships.
|
E217497
|
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: Bobby Leonard | Statement: [Indiana Pacers (ABA), headCoach, Bobby Leonard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bobby Leonard Context triple: [Indiana Pacers (ABA), headCoach, Bobby Leonard]
-
A.
Bobby Reed
Bobby Reed was the husband of actress and vaudeville performer June Havoc.
-
B.
Bobby Morrow
Bobby Morrow was an American sprinter and three-time Olympic gold medalist renowned for dominating the 100m and 200m events in the mid-1950s.
-
C.
Bobby Dyer
Bobby Dyer is an American politician who serves as the mayor of Virginia Beach, Virginia.
-
D.
Bobby Byrne
Bobby Byrne is an American cinematographer known for his work on films such as the baseball romantic comedy "Bull Durham."
-
E.
Bobby Bell
Bobby Bell is a legendary American football linebacker and defensive end renowned for his dominant play with the Kansas City Chiefs and induction into the Pro Football Hall of Fame.
- 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: Bobby Leonard Triple: [Indiana Pacers (ABA), headCoach, Bobby Leonard]
Generated description
Bobby Leonard was a Hall of Fame basketball coach and former player best known for leading the Indiana Pacers to multiple ABA championships.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bobby Leonard Target entity description: Bobby Leonard was a Hall of Fame basketball coach and former player best known for leading the Indiana Pacers to multiple ABA championships.
-
A.
Bobby Reed
Bobby Reed was the husband of actress and vaudeville performer June Havoc.
-
B.
Bobby Morrow
Bobby Morrow was an American sprinter and three-time Olympic gold medalist renowned for dominating the 100m and 200m events in the mid-1950s.
-
C.
Bobby Dyer
Bobby Dyer is an American politician who serves as the mayor of Virginia Beach, Virginia.
-
D.
Bobby Byrne
Bobby Byrne is an American cinematographer known for his work on films such as the baseball romantic comedy "Bull Durham."
-
E.
Bobby Bell
Bobby Bell is a legendary American football linebacker and defensive end renowned for his dominant play with the Kansas City Chiefs and induction into the Pro Football Hall of Fame.
- 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_69a493c53e648190ae8cb76c433fd9a7 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b4fb18b88190ae2d620aaaff4f90 |
completed | March 1, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf3a5cac081908d655e42a58a81c5 |
completed | March 8, 2026, 10:09 p.m. |
| NEDg | Description generation | batch_69adf55207248190bd903c7faa3729e7 |
completed | March 8, 2026, 10:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adf5e074088190b6b383fb93cd18d8 |
completed | March 8, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:41 p.m.