Triple

T14476921
Position Surface form Disambiguated ID Type / Status
Subject Come and Go E358996 entity
Predicate notableInterpreter P22732 FINISHED
Object Billie Whitelaw E72700 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: Billie Whitelaw | Statement: [Come and Go, notableInterpreter, Billie Whitelaw]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Billie Whitelaw
Context triple: [Come and Go, notableInterpreter, Billie Whitelaw]
  • A. Billie Whitelaw chosen
    Billie Whitelaw was an acclaimed English actress renowned for her intense stage and screen performances, particularly in the plays of Samuel Beckett.
  • B. Peggy Ashcroft
    Peggy Ashcroft was a distinguished English stage and film actress renowned for her Shakespearean performances and her long, influential career in British theatre.
  • C. Celia Johnson
    Celia Johnson was a distinguished English actress best known for her nuanced, understated performances in classic British films such as "Brief Encounter."
  • D. Edith Lesley
    Edith Lesley was an American educator and founder of the teacher-training institution that evolved into Lesley University in Cambridge, Massachusetts.
  • E. Anna Massey
    Anna Massey was an acclaimed English actress known for her nuanced performances in film, television, and theatre, including notable roles in psychological dramas and literary adaptations.
  • 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_69d827966698819082e140837737501d completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de9248edb48190a74eb032aeaac027 completed April 14, 2026, 7:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69feef5c11708190a7fd4c0682b6ed81 completed May 9, 2026, 8:25 a.m.
Created at: April 10, 2026, 1:20 a.m.