Triple

T8207026
Position Surface form Disambiguated ID Type / Status
Subject Lady for a Day E191712 entity
Predicate stars P1956 FINISHED
Object Warren William E129338 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: Warren William | Statement: [Lady for a Day, stars, Warren William]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Warren William
Context triple: [Lady for a Day, stars, Warren William]
  • A. Warren William chosen
    Warren William was an American stage and film actor of the 1930s, best known for his suave, often morally ambiguous leading and supporting roles in Hollywood pre-Code dramas and mysteries.
  • B. Warner Baxter
    Warner Baxter was an American film actor best known for his Academy Award–winning performance in the 1928 film "In Old Arizona" and for his roles in early sound-era Hollywood dramas and crime films.
  • C. Harry Davenport
    Harry Davenport was an American character actor best known for his numerous supporting roles in classic Hollywood films of the 1930s and 1940s.
  • D. Warner Oland
    Warner Oland was a Swedish-American actor best known for portraying the detective Charlie Chan in a popular series of 1930s films.
  • E. Boyd Holbrook
    Boyd Holbrook is an American actor and former model known for roles in films like "Logan" and "Gone Girl" and the Netflix series "Narcos."
  • 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_69ca82c7f3e08190857bf1fc63b2a10c completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb726b520081908ce4a03bd14dfcdf completed March 31, 2026, 7:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce023e93248190abbe3cd8ab8194fd completed April 2, 2026, 5:44 a.m.
Created at: March 30, 2026, 5:43 p.m.