Triple

T11390623
Position Surface form Disambiguated ID Type / Status
Subject Errol Brown E269825 entity
Predicate givenName P17 FINISHED
Object Errol E54460 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: Errol | Statement: [Errol Brown, givenName, Errol]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Errol
Context triple: [Errol Brown, givenName, Errol]
  • A. Errol chosen
    Errol is a masculine given name of English origin, often used as a first name in various English-speaking countries.
  • B. Errol Thompson
    Errol Thompson was a pioneering Jamaican recording engineer and producer known for his influential work in reggae and dub music during the 1970s and 1980s.
  • C. Rex Harrington
    Rex Harrington is a celebrated Canadian ballet dancer and former principal with the National Ballet of Canada, renowned for his dramatic stage presence and virtuosic technique.
  • D. Leon Errol
    Leon Errol was an Australian-born American comedian and character actor best known for his rubber-legged physical comedy and prolific work in vaudeville, Broadway, and Hollywood films of the early 20th century.
  • E. Joe E. Brown
    Joe E. Brown was a popular American comedian and film actor of the 1930s and 1940s, known for his wide-mouthed grin and roles in numerous Hollywood comedies.
  • 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_69d6aacdbc6c8190af6dc3d5f5d22836 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d800160a1c81909d115bf89fe54a49 completed April 9, 2026, 7:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69e58c8f5ed88190b9cc55c0a73993ec completed April 20, 2026, 2:16 a.m.
Created at: April 8, 2026, 9:34 p.m.