Triple
T20029086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Because of Winn-Dixie |
E495071
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Trevor Albert |
—
|
NE NERFINISHED |
How this triple was built (2 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: Trevor Albert | Statement: [Because of Winn-Dixie, producer, Trevor Albert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trevor Albert Context triple: [Because of Winn-Dixie, producer, Trevor Albert]
-
A.
Trevor Albert
chosen
Trevor Albert is a film producer best known for his work on the classic comedy "Groundhog Day."
-
B.
Trevor Rich
Trevor Rich is a hip-hop artist known for his featured appearance on the track "Born 2 Rap."
-
C.
Trevor Blackwell
Trevor Blackwell is a Canadian engineer, entrepreneur, and roboticist best known as a co-founder of the startup accelerator Y Combinator and for his work in humanoid and self-balancing robots.
-
D.
Trevor Hampton
Trevor Hampton is an individual notable enough to be recognized as a prominent bearer of the surname Hampton.
-
E.
Trevor Darrell
Trevor Darrell is a prominent computer vision and machine learning researcher and professor known for his work on deep learning, visual recognition, and autonomous systems.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e662908df081909a6c8ccf0dd90fff |
completed | April 20, 2026, 5:29 p.m. |
Created at: April 11, 2026, 3:36 p.m.