Triple
T7507479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bates |
E177426
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Bill Bates
Bill Bates is a former American football safety best known for his long and successful career with the Dallas Cowboys in the NFL.
|
E668936
|
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: Bill Bates | Statement: [Bates, hasNotableBearer, Bill Bates]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bill Bates Context triple: [Bates, hasNotableBearer, Bill Bates]
-
A.
Alan Baxter
Alan Baxter was an American character actor known for his roles in mid-20th-century film and television, often portraying tough or villainous figures.
-
B.
Clifton Daniel
Clifton Daniel was an American newspaper editor and managing editor of The New York Times, known also as the son-in-law of U.S. President Harry S. Truman.
-
C.
Donald Bates
Donald Bates is an Australian architect best known as a co-designer of Melbourne’s landmark Federation Square complex.
-
D.
Robert Parrish
Robert Parrish was an American film editor and director, as well as a former child actor, known for his work on several classic Hollywood films.
-
E.
Errol Thompson
Errol Thompson was a pioneering Jamaican recording engineer and producer known for his influential work in reggae and dub music during the 1970s and 1980s.
- 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: Bill Bates Triple: [Bates, hasNotableBearer, Bill Bates]
Generated description
Bill Bates is a former American football safety best known for his long and successful career with the Dallas Cowboys in the NFL.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bill Bates Target entity description: Bill Bates is a former American football safety best known for his long and successful career with the Dallas Cowboys in the NFL.
-
A.
Alan Baxter
Alan Baxter was an American character actor known for his roles in mid-20th-century film and television, often portraying tough or villainous figures.
-
B.
Clifton Daniel
Clifton Daniel was an American newspaper editor and managing editor of The New York Times, known also as the son-in-law of U.S. President Harry S. Truman.
-
C.
Donald Bates
Donald Bates is an Australian architect best known as a co-designer of Melbourne’s landmark Federation Square complex.
-
D.
Robert Parrish
Robert Parrish was an American film editor and director, as well as a former child actor, known for his work on several classic Hollywood films.
-
E.
Errol Thompson
Errol Thompson was a pioneering Jamaican recording engineer and producer known for his influential work in reggae and dub music during the 1970s and 1980s.
- 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_69c69f276b108190af2cc790b6554544 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5b76a288190bb3608a5e3bfa212 |
completed | March 27, 2026, 9:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c83ca56e3c81908c3bae8ad2d9ecd1 |
completed | March 28, 2026, 8:40 p.m. |
| NEDg | Description generation | batch_69c83d5884a88190a22c0fb54f9731c7 |
completed | March 28, 2026, 8:43 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c84071abb88190a2dadd57ff088f3d |
completed | March 28, 2026, 8:56 p.m. |
Created at: March 27, 2026, 3:45 p.m.