Triple
T13575792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeff Nickell |
E324278
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Jeff 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: Jeff Nickell | Statement: [Jeff Nickell, name, Jeff Nickell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeff Nickell Context triple: [Jeff Nickell, name, Jeff 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.
Brian Routh
Brian Routh was a British performance artist and musician best known as one half of the avant-garde comedy and performance duo The Kipper Kids.
-
D.
Keith Poulson
Keith Poulson is an American actor known for his work in independent films and for roles in offbeat, character-driven movies.
-
E.
Greg Latta
Greg Latta was an American professional football tight end who played in the World Football League and the NFL, most notably for the Chicago Bears.
- 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_69d80769100c819099111274614f5ed2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb02b1f108190a12af382d1de70bb |
completed | April 12, 2026, 2:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a838581c819092a195f60673b743 |
completed | May 3, 2026, 7:55 p.m. |
Created at: April 9, 2026, 9:48 p.m.