Triple

T13014187
Position Surface form Disambiguated ID Type / Status
Subject Irving Willat E322501 entity
Predicate directed P7373 FINISHED
Object The Man Who Won E1015105 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: The Man Who Won | Statement: [Irving Willat, directed, The Man Who Won]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: The Man Who Won
Context triple: [Irving Willat, directed, The Man Who Won]
  • A. The Man Who Won chosen
    The Man Who Won is a 1923 American silent Western film directed by Irving Cummings and produced by Irving Willat.
  • B. The Man Who Had Everything
    The Man Who Had Everything is a 1920s American silent film drama featuring actress Priscilla Bonner in a prominent role.
  • C. The Man at Six
    The Man at Six is a British mystery play, best known as a stage thriller by actor-playwright Frank Vosper.
  • D. The Man Who Dared
    The Man Who Dared is a 1946 American crime drama film featuring actress Tala Birell in a prominent role.
  • E. The Autograph Man
    The Autograph Man is a novel by Zadie Smith that satirically explores celebrity culture, identity, and obsession through the life of a professional autograph collector in contemporary London.
  • 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_69d807657e8c8190bd9435ee2f823845 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97ecbb8f4819094d55eb07cb5ad97 completed April 10, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6cbc77f308190b3b47f7a092db434 completed May 3, 2026, 4:15 a.m.
Created at: April 9, 2026, 8:50 p.m.