Triple
T13166493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | It's My Turn |
E312862
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Jill Clayburgh |
E259556
|
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: Jill Clayburgh | Statement: [It's My Turn, stars, Jill Clayburgh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jill Clayburgh Context triple: [It's My Turn, stars, Jill Clayburgh]
-
A.
Jill Clayburgh
chosen
Jill Clayburgh was an American actress acclaimed for her intelligent, nuanced performances in 1970s and 1980s films, including multiple Academy Award–nominated roles.
-
B.
Karen Kline
Karen Kline is an American psychotherapist best known as the longtime spouse of Academy Award–winning actress Linda Hunt.
-
C.
Elisabeth Shue
Elisabeth Shue is an American actress known for her roles in films such as "The Karate Kid," "Adventures in Babysitting," and "Leaving Las Vegas," for which she received an Academy Award nomination.
-
D.
Catherine Cusack
Catherine Cusack is an Irish actress known for her work in theatre, film, and television, and as a member of the prominent Cusack acting family.
-
E.
Dianne Wiest
Dianne Wiest is an acclaimed American actress known for her versatile performances in film, television, and theater, including multiple award-winning supporting roles.
- 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_69d806ac3ee081909b2fd27d060aa974 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c2c317881908cc715c97d915f77 |
completed | April 10, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f77f7696f0819094825b158e1e9a8c |
completed | May 3, 2026, 5:01 p.m. |
Created at: April 9, 2026, 9:13 p.m.