Triple

T9802159
Position Surface form Disambiguated ID Type / Status
Subject John Woo E237863 entity
Predicate directed P7373 FINISHED
Object Hard Target E379697 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: Hard Target | Statement: [John Woo, directed, Hard Target]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hard Target
Context triple: [John Woo, directed, Hard Target]
  • A. Hard Target chosen
    Hard Target is a 1993 American action film directed by John Woo and starring Jean-Claude Van Damme, known for its stylized violence and Woo’s Hollywood debut.
  • B. Red Heat
    Red Heat is a 1988 buddy-cop action film starring Arnold Schwarzenegger and James Belushi, directed by Walter Hill.
  • C. Point Blank
    Point Blank is a segment or component of the larger work titled "The River," likely representing a distinct chapter, track, or section within that overall composition.
  • D. Point Blank
    Point Blank is a 1967 neo-noir crime film starring Lee Marvin, noted for its stylish direction and influential, hard-edged portrayal of revenge.
  • E. Crime Pays
    Crime Pays is a 2009 studio album by Harlem rapper Cam'ron, known for its gritty street narratives and return-to-form sound.
  • 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_69ca84dd4608819097ff4ed00feca280 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cda62b41048190bcef70a7591830c6 completed April 1, 2026, 11:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1c44edac48190a44fdfb858d0dbba completed April 5, 2026, 2:09 a.m.
Created at: March 30, 2026, 8:29 p.m.