Triple

T20598965
Position Surface form Disambiguated ID Type / Status
Subject Arthur Grant E506122 entity
Predicate name P16 FINISHED
Object Arthur Grant 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: Arthur Grant | Statement: [Arthur Grant, name, Arthur Grant]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arthur Grant
Context triple: [Arthur Grant, name, Arthur Grant]
  • A. Arthur Grant chosen
    Arthur Grant was a British cinematographer best known for his work on numerous Hammer Films productions in the mid-20th century.
  • B. Keith Grant
    Keith Grant was a renowned British recording engineer celebrated for his influential work at London’s Olympic Studios with many major rock and pop artists of the 1960s and 1970s.
  • C. Garth Stevenson
    Garth Stevenson is a Canadian-born double bassist and composer known for his atmospheric, nature-inspired film scores and solo work.
  • D. Robert Grant
    Robert Grant is the harried but good-hearted hotel manager and central human protagonist in the family comedy film "Dunston Checks In."
  • E. Don Williamson
    Don Williamson was an American businessman and politician who served as the controversial mayor of Flint, Michigan in the 2000s.
  • 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_69e0b4ba6ae88190af871e1f9522c704 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6aa1e251c8190926dafe1402eb63c completed April 20, 2026, 10:35 p.m.
Created at: April 16, 2026, 11:40 a.m.