Triple

T11923119
Position Surface form Disambiguated ID Type / Status
Subject Zora Arkus-Duntov E283707 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: [Zora Arkus-Duntov, employer, General Motors]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: General Motors
Context triple: [Zora Arkus-Duntov, 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_69d6ab2ce9c48190b5d39511b524f666 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8e8e2fc648190a446c1917db1c7d9 completed April 10, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69f44042cf1c81909de44acfe1202482 completed May 1, 2026, 5:55 a.m.
Created at: April 8, 2026, 9:45 p.m.