Triple
T10188960
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | You Can Count on Me |
E237982
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Jeff Sharp |
E118536
|
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 Sharp | Statement: [You Can Count on Me, producer, Jeff Sharp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeff Sharp Context triple: [You Can Count on Me, producer, Jeff Sharp]
-
A.
Jeff Sharp
chosen
Jeff Sharp is a film producer known for his work on independent and literary adaptation projects in American cinema.
-
B.
Brian Sharp
Brian Sharp is a relatively obscure individual whose primary distinguishing feature is sharing the surname "Sharp" rather than broad public recognition for specific achievements.
-
C.
Paul Sturgeon
Paul Sturgeon is a relatively obscure individual whose primary distinguishing feature is sharing the surname Sturgeon, with no widely documented public achievements or roles.
-
D.
Fred Kilgour
Fred Kilgour was an American librarian and information scientist best known for pioneering online library cataloging and founding the OCLC cooperative.
-
E.
Andrew Gunn
Andrew Gunn is an American film producer known for his work on family-oriented and teen-focused movies, including several popular Disney films.
- 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_69ca84de1b208190bf17bb305b002605 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cded7c3278819093312665b54d888c |
completed | April 2, 2026, 4:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d317b734a4819085645caea8ba0481 |
completed | April 6, 2026, 2:17 a.m. |
Created at: March 30, 2026, 9:12 p.m.