Triple

T14398355
Position Surface form Disambiguated ID Type / Status
Subject Dirty Sexy Money E357007 entity
Predicate starring P1507 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: [Dirty Sexy Money, starring, Jill Clayburgh]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jill Clayburgh
Context triple: [Dirty Sexy Money, starring, 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_69d827927c988190ad98bb0360981783 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de9083f9d081908fe5c99655c410b3 completed April 14, 2026, 7:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe9db810c481908dde925ff90f3fa0 completed May 9, 2026, 2:36 a.m.
Created at: April 10, 2026, 1:17 a.m.