Triple

T17026183
Position Surface form Disambiguated ID Type / Status
Subject Golden Harvest E413068 entity
Predicate collaboratedWith P435 FINISHED
Object Bruce Lee E273351 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: Bruce Lee | Statement: [Golden Harvest, collaboratedWith, Bruce Lee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bruce Lee
Context triple: [Golden Harvest, collaboratedWith, Bruce Lee]
  • A. Bruce Lee chosen
    Bruce Lee was a legendary Hong Kong-American martial artist, actor, and filmmaker who revolutionized martial arts cinema and popularized kung fu worldwide.
  • B. Brandon Lee
    Brandon Lee was an American actor and martial artist, best known for his lead role in the film "The Crow" and for being the son of Bruce Lee.
  • C. Jackie Chan
    Jackie Chan is a Hong Kong martial artist, actor, stuntman, and filmmaker renowned worldwide for his acrobatic fighting style, innovative stunts, and blend of action and comedy in films.
  • D. Keye Luke
    Keye Luke was a Chinese-American actor and artist best known for his roles in the Charlie Chan films, the original "Kung Fu" TV series, and as Master Po in "Kung Fu."
  • E. Jimmy Lei Ba
    Jimmy Lei Ba is a machine learning researcher known for influential contributions to deep learning optimization and normalization techniques, including the development of Layer Normalization.
  • 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_69d886cc4170819093deddc7b8b4b6a7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d5d46a5081908bc5681621dd8534 completed April 18, 2026, 7:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a012334c3b48190b125ab926450c45b completed May 11, 2026, 12:30 a.m.
Created at: April 10, 2026, 5:33 a.m.