Triple
T10169483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Geoff Collins |
E235292
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Geoff Collins |
E235292
|
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: Geoff Collins | Statement: [Geoff Collins, name, Geoff Collins]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Geoff Collins Context triple: [Geoff Collins, name, Geoff Collins]
-
A.
Geoff Collins
chosen
Geoff Collins is an American football coach best known for his head coaching stints at Temple University and Georgia Tech, as well as his defensive coaching roles at several major college programs.
-
B.
Paul Collins
Paul Collins is a voice actor best known for providing the voice of Peter Pan in animated adaptations.
-
C.
Geoff Pierson
Geoff Pierson is an American actor known for his work in television dramas and comedies, including prominent roles on shows like Dexter and Unhappily Ever After.
-
D.
Geoff Hall
Geoff Hall is a cinematographer known for his work on the Australian film "Red Dog."
-
E.
Steve Collins
Steve Collins is a fictional character appearing in the classic 1941 screwball comedy film "The Bride Came C.O.D."
- 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_69ca84ceafd0819085828600e11bed6b |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdec9ba56481908b5265aea8ea8cbe |
completed | April 2, 2026, 4:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d74fce236481909387829c7cf311a4 |
completed | April 9, 2026, 7:05 a.m. |
Created at: March 30, 2026, 9:10 p.m.