Triple

T9759420
Position Surface form Disambiguated ID Type / Status
Subject Lili E236631 entity
Predicate costumeDesignBy P184 FINISHED
Object Helen Rose E89105 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: Helen Rose | Statement: [Lili, costumeDesignBy, Helen Rose]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Helen Rose
Context triple: [Lili, costumeDesignBy, Helen Rose]
  • A. Helen Rose chosen
    Helen Rose was an acclaimed American costume designer best known for her glamorous work at MGM during Hollywood’s Golden Age, creating iconic wardrobes for stars like Elizabeth Taylor and Grace Kelly.
  • B. Hellen Rose
    Hellen Rose is an Australian multidisciplinary artist, performer, and filmmaker known for her collaborative work in politically engaged and experimental art.
  • C. Helen Hughes
    Helen Hughes was a daughter of Charles Evans Hughes, the prominent American statesman who served as both U.S. Secretary of State and Chief Justice of the Supreme Court.
  • D. Helen Willis
    Helen Willis is a central character on the sitcom "The Jeffersons," known as Louise Jefferson’s close friend and one half of the show’s groundbreaking interracial couple.
  • E. Helen Shay
    Helen Shay was the wife of American character actor Guy Kibbee, known for his roles in 1930s and 1940s Hollywood films.
  • 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_69ca84d64f6c8190a4ed4e9f5936eda5 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cda049995c81908569ec61805642b2 completed April 1, 2026, 10:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69e42d406e2c8190ad27f3a5276be25f completed April 19, 2026, 1:17 a.m.
Created at: March 30, 2026, 8:24 p.m.