Triple

T5589666
Position Surface form Disambiguated ID Type / Status
Subject Greyhound Scenicruiser bus styling E146843 entity
Predicate associatedWith P37 FINISHED
Object General Motors E506 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: General Motors | Statement: [Greyhound Scenicruiser bus styling, associatedWith, General Motors]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: General Motors
Context triple: [Greyhound Scenicruiser bus styling, associatedWith, General Motors]
  • A. General Motors chosen
    General Motors is a major American multinational automotive manufacturer known for brands such as Chevrolet, GMC, Cadillac, and Buick.
  • B. Ford Motor Company
    Ford Motor Company is a major American automobile manufacturer, founded by Henry Ford, known for pioneering assembly-line mass production and producing iconic vehicles like the Model T and F-Series trucks.
  • C. Ford
    Ford is a town in the Metropolitan Borough of Sefton, Merseyside, England, forming part of the northern suburbs of Liverpool.
  • D. Ford
    Ford is a common English surname borne by numerous notable individuals, including U.S. President Gerald Ford.
  • E. Chevrolet
    Chevrolet is a major American automobile marque known for producing a wide range of affordable cars, trucks, and SUVs.
  • 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_69c009036c408190981a8d690b679b67 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c0209ff5d88190843b6d134390ab71 completed March 22, 2026, 5:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07d85e478819087502c3927997363 completed March 22, 2026, 11:38 p.m.
Created at: March 22, 2026, 3:38 p.m.