Triple

T10776762
Position Surface form Disambiguated ID Type / Status
Subject Soon-Yi Previn E254215 entity
Predicate sibling P363 FINISHED
Object Tam Farrow E551642 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: Tam Farrow | Statement: [Soon-Yi Previn, sibling, Tam Farrow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tam Farrow
Context triple: [Soon-Yi Previn, sibling, Tam Farrow]
  • A. Michael Macdonald Farrow
    Michael Macdonald Farrow is the son of American silent film actress Lila Lee.
  • B. Tisa Farrow chosen
    Tisa Farrow is an American actress known for her roles in 1970s and 1980s films, including several cult horror and exploitation movies.
  • C. Mia Farrow
    Mia Farrow is an American actress and humanitarian known for her roles in films like "Rosemary's Baby" and for her extensive advocacy work with UNICEF.
  • D. Stephanie Farrow
    Stephanie Farrow is an American actress and model best known as the younger sister of actress Mia Farrow.
  • E. María de Lourdes Villiers Farrow
    María de Lourdes Villiers Farrow, better known as Mia Farrow, is an American actress and humanitarian renowned for her roles in films like "Rosemary's Baby" and her extensive advocacy work, particularly with UNICEF.
  • 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_69d6aa609f008190a294200aefcb7bd5 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7329d8c908190bddad40685133ea1 completed April 9, 2026, 5:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69e2162f1f648190b325c7e7647b543e completed April 17, 2026, 11:14 a.m.
Created at: April 8, 2026, 9:16 p.m.