Triple

T22033668
Position Surface form Disambiguated ID Type / Status
Subject María de Lourdes Villiers Farrow E544147 entity
Predicate familyName P18 FINISHED
Object Farrow NE NERFINISHED

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: Farrow | Statement: [María de Lourdes Villiers Farrow, familyName, Farrow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Farrow
Context triple: [María de Lourdes Villiers Farrow, familyName, Farrow]
  • A. Farrow chosen
    Farrow is the surname of American actress and humanitarian Mia Farrow, known for her work in film and activism.
  • B. Farrah
    Farrah is a feminine given name most notably associated with American reality television personality and author Farrah Abraham.
  • C. Farrah
    Farrah is a surname of Arabic origin borne by various individuals, including Hussein Mohamed Farrah.
  • D. Farr
    Farr is a surname of English and Scottish origin borne by various notable individuals, including musicians, actors, and public figures.
  • E. Tisa Farrow
    Tisa Farrow is an American actress known for her roles in 1970s and 1980s films, including several cult horror and exploitation movies.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e2f98c8819083e11eab90942a78 completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f127ef97348190b8dcdcad11694ebe completed April 28, 2026, 9:34 p.m.
Created at: April 16, 2026, 8:24 p.m.