Triple

T6634595
Position Surface form Disambiguated ID Type / Status
Subject Woman (film) E150415 entity
Predicate title P38 FINISHED
Object Woman E150415 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: Woman | Statement: [Woman (film), title, Woman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Woman
Context triple: [Woman (film), title, Woman]
  • A. Woman chosen
    Woman is a documentary film by Yann Arthus-Bertrand that presents intimate interviews with women around the world, exploring their experiences, challenges, and perspectives.
  • B. Woman III
    Woman III is an abstract expressionist painting by Willem de Kooning, renowned for its aggressive, gestural depiction of a female figure and its pivotal role in his celebrated "Women" series.
  • C. Women
    "Women" is a semi-autobiographical novel by Charles Bukowski that follows his hard-drinking alter ego Henry Chinaski through a series of raw, often chaotic relationships with various women.
  • D. Mrs
    Mrs is a common English honorific used as a title for married women.
  • E. Woman I
    Woman I is a landmark abstract expressionist painting by Willem de Kooning, renowned for its aggressive brushwork and provocative depiction of the female figure.
  • 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_69c687f0ceb08190bf40807bfc605fa5 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6afcc1c9c819087fcde19a5d49fd2 completed March 27, 2026, 4:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6cbf329f08190a3f29c4d4c6aa136 completed March 27, 2026, 6:26 p.m.
Created at: March 27, 2026, 1:59 p.m.