Triple

T15063527
Position Surface form Disambiguated ID Type / Status
Subject Hard Target E379697 entity
Predicate director P255 FINISHED
Object John Woo E237863 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: John Woo | Statement: [Hard Target, director, John Woo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Woo
Context triple: [Hard Target, director, John Woo]
  • A. John Woo chosen
    John Woo is a renowned Hong Kong-born film director and producer best known for his highly stylized action movies featuring balletic gunplay and intense emotional drama.
  • B. Tsui Hark
    Tsui Hark is a pioneering Hong Kong filmmaker renowned for revolutionizing the action and wuxia genres with his visually inventive, high-energy directing style.
  • C. Dennis Dun
    Dennis Dun is an American actor best known for his roles in films like "Big Trouble in Little China" and "The Last Emperor," as well as various television appearances.
  • D. Kim Jee-woon
    Kim Jee-woon is a South Korean film director and screenwriter known for his stylish, genre-spanning works such as "A Tale of Two Sisters," "A Bittersweet Life," and "I Saw the Devil."
  • E. Richard LaGravenese
    Richard LaGravenese is an American screenwriter and director known for his character-driven dramas and adaptations, including films like "The Fisher King," "The Bridges of Madison County," and "P.S. I Love You."
  • 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_69d85cd7683881908d405c1b5d7b4f7f completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69dedee803ac81908bb7d66e49c2eb72 completed April 15, 2026, 12:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69fea5c8b3ac8190b8fc921b6e6eeed5 completed May 9, 2026, 3:11 a.m.
Created at: April 10, 2026, 3:02 a.m.