Triple
T5433545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paper Moon |
E121552
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Cybill Shepherd |
E154558
|
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: Cybill Shepherd | Statement: [Paper Moon, producer, Cybill Shepherd]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cybill Shepherd Context triple: [Paper Moon, producer, Cybill Shepherd]
-
A.
Cybill Shepherd
chosen
Cybill Shepherd is an American actress and former model best known for her roles in films like "The Last Picture Show" and the TV series "Moonlighting."
-
B.
Jorja Fox
Jorja Fox is an American actress best known for her long-running role as Sara Sidle on the television series CSI: Crime Scene Investigation.
-
C.
Tyne Daly
Tyne Daly is an American actress acclaimed for her powerful performances in television dramas, film, and theater, including her iconic role in the series "Cagney & Lacey."
-
D.
Elizabeth Berkley
Elizabeth Berkley is an American actress best known for her roles in the TV series "Saved by the Bell" and the film "Showgirls."
-
E.
Nancy Walker
Nancy Walker was an American actress and comedian best known for her sharp-tongued character roles in film, television, and Broadway musicals.
- 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_69bd463c65f0819082ee6483ab4b466a |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd91ae18cc8190aefe610f91b5382c |
completed | March 20, 2026, 6:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf858027b08190a6ea46493c6a87c6 |
completed | March 22, 2026, 6 a.m. |
Created at: March 20, 2026, 2:06 p.m.