Triple

T6538675
Position Surface form Disambiguated ID Type / Status
Subject Laura (film) E168229 entity
Predicate starring P1507 FINISHED
Object Dana Andrews E391255 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: Dana Andrews | Statement: [Laura (film), starring, Dana Andrews]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dana Andrews
Context triple: [Laura (film), starring, Dana Andrews]
  • A. Dana Andrews chosen
    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."
  • 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. Farley Granger
    Farley Granger was an American actor best known for his roles in classic mid-20th-century films such as Alfred Hitchcock’s "Rope" and "Strangers on a Train."
  • 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. Glenn Ford
    Glenn Ford was a Canadian-American film actor renowned for his versatile performances in classic Hollywood movies such as "Gilda," "The Big Heat," and "Blackboard Jungle."
  • 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_69c68a51564081909e93aee0dbd9cca3 completed March 27, 2026, 1:46 p.m.
NER Named-entity recognition batch_69c6add4b7f881909e485325c353f51c completed March 27, 2026, 4:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7488992c48190a59b277f65cd0b36 completed March 28, 2026, 3:18 a.m.
Created at: March 27, 2026, 1:49 p.m.