Triple
T2189334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nickell |
E49824
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Phil Nickell |
E324278
|
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: Phil Nickell | Statement: [Nickell, hasNotableBearer, Phil Nickell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Phil Nickell Context triple: [Nickell, hasNotableBearer, Phil Nickell]
-
A.
Jeff Nickell
chosen
Jeff Nickell is an individual notable enough to be specifically referenced as a bearer of the surname Nickell.
-
B.
Kevin Nolting
Kevin Nolting is an American film editor best known for his work on Pixar animated features, including the Academy Award-winning film "Up."
-
C.
Kevin Hageman
Kevin Hageman is an American screenwriter and producer known for his work on animated and family films and television series, including contributions to The Lego Movie franchise.
-
D.
Kevin Chapman
Kevin Chapman is an American actor known for his tough, blue-collar character roles in film and television, including prominent parts in series like "Person of Interest" and "City on a Hill."
-
E.
Kevin Gage
Kevin Gage is an American actor best known for his intense supporting roles in films such as "Heat" and "G.I. Jane."
- 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_69a88aaba3c48190b351cab9b26989ff |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abbf38f70081909f442eed226a282e |
completed | March 7, 2026, 6:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b333f7b3848190bcef4a06a2b94dd1 |
completed | March 12, 2026, 9:45 p.m. |
Created at: March 4, 2026, 7:46 p.m.