Triple

T8409883
Position Surface form Disambiguated ID Type / Status
Subject Robert A. Frosch E198594 entity
Predicate employer P7 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: [Robert A. Frosch, employer, General Motors]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: General Motors
Context triple: [Robert A. Frosch, employer, 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 small village in the Arun District of West Sussex, England, known for its rural character and nearby railway station.
  • E. Ford
    Ford is a common English surname borne by numerous notable individuals, including U.S. President Gerald Ford.
  • 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_69ca831201b481909e137936ef99ff11 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cb8317045c8190b69cc99854b633be completed March 31, 2026, 8:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce0317cb188190b207bcaffb629a75 completed April 2, 2026, 5:48 a.m.
Created at: March 30, 2026, 6:05 p.m.