Triple

T22760527
Position Surface form Disambiguated ID Type / Status
Subject Mister Johnson E562974 entity
Predicate screenwriter P2831 FINISHED
Object William Boyd 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: William Boyd | Statement: [Mister Johnson, screenwriter, William Boyd]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: William Boyd
Context triple: [Mister Johnson, screenwriter, William Boyd]
  • A. William Boyd chosen
    William Boyd is a Scottish novelist and screenwriter acclaimed for his richly crafted literary fiction, including works such as "Any Human Heart" and "A Good Man in Africa."
  • B. William Boyd
    William Boyd was an American actor best known for portraying the cowboy hero Hopalong Cassidy in numerous films and early television.
  • C. David de Kretser
    David de Kretser is an Australian medical researcher and academic who served as the 27th Governor of Victoria.
  • D. Louis de Bernières
    Louis de Bernières is a British novelist best known for works such as "Captain Corelli’s Mandolin" and the novella "Red Dog," which inspired the film of the same name.
  • E. Gregg Mayles
    Gregg Mayles is a British video game designer best known for his long-time work at Rare on classic titles such as the Donkey Kong Country series and Banjo-Kazooie.
  • 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_69e24552e11c81909c2d61578a558bd7 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17a7c45b881908b29ba1439038789 completed April 29, 2026, 3:26 a.m.
Created at: April 17, 2026, 3:26 p.m.