Triple

T23492082
Position Surface form Disambiguated ID Type / Status
Subject Too Many Girls E570702 entity
Predicate featuresPerformer P1363 FINISHED
Object Van Johnson NE NERFINISHED

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: Van Johnson | Statement: [Too Many Girls, featuresPerformer, Van Johnson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Van Johnson
Context triple: [Too Many Girls, featuresPerformer, Van Johnson]
  • A. Van Johnson chosen
    Van Johnson was a popular American film and television actor of the 1940s and 1950s, known for his boy-next-door charm and roles in wartime dramas and musicals.
  • B. Robert Cummings
    Robert Cummings was an American film and television actor best known for his roles in comedies and thrillers during Hollywood’s Golden Age.
  • C. Stuart Erwin
    Stuart Erwin was an American actor known for his work in early 20th-century film, radio, and television, often portraying affable, comedic everyman characters.
  • D. William Holden
    William Holden was an acclaimed American film actor known for his charismatic performances in classics such as "Sunset Boulevard," "Stalag 17," and "The Bridge on the River Kwai."
  • E. Dana Andrews
    Dana Andrews was a prominent American film actor of the 1940s and 1950s, best known for his leading roles in classics such as "Laura" and "The Best Years of Our Lives."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245b0b01481908f636939bedd804c completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1a7dd56408190b459077e433ed1c3 completed April 29, 2026, 6:40 a.m.
Created at: April 17, 2026, 6:05 p.m.