Triple
T2834325
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Finch |
E62313
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Kevin Misher |
E314388
|
NE FINISHED |
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: Kevin Misher | Statement: [Finch, producer, Kevin Misher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kevin Misher Context triple: [Finch, producer, Kevin Misher]
-
A.
Kevin Misher
chosen
Kevin Misher is an American film producer and former studio executive known for overseeing and producing a range of major Hollywood movies across genres.
-
B.
Kevin Cossom
Kevin Cossom is an American singer, songwriter, and record producer known for his R&B and hip-hop collaborations and songwriting for major artists.
-
C.
Eric McLeod
Eric McLeod is a film producer known for his work on major Hollywood action and genre movies, including the monster crossover blockbuster "Godzilla vs. Kong."
-
D.
Kevin Yagher
Kevin Yagher is an American special effects and makeup artist and director best known for his work on horror and fantasy films and for creating iconic genre characters.
-
E.
Michael Potts
Michael Potts is an American actor known for his work in film, television, and theater, including notable roles in projects like "The Wire," "True Detective," and various Broadway productions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ab4c3c39188190955b9c49d98463d8 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdec18b808190aedae2ed11d53b15 |
completed | March 7, 2026, 8:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4881323dc81908e3e42ce1d0f8a0d |
completed | March 13, 2026, 9:56 p.m. |
Created at: March 6, 2026, 10:01 p.m.