Triple

T10908581
Position Surface form Disambiguated ID Type / Status
Subject Merkur (automobile marque) E257633 entity
Predicate parentOrganization P254 FINISHED
Object Ford Motor Company E1133 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: Ford Motor Company | Statement: [Merkur (automobile marque), parentOrganization, Ford Motor Company]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ford Motor Company
Context triple: [Merkur (automobile marque), parentOrganization, Ford Motor Company]
  • A. Ford Motor Company chosen
    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.
  • B. General Motors
    General Motors is a major American multinational automotive manufacturer known for brands such as Chevrolet, GMC, Cadillac, and Buick.
  • 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. Ford
    Ford is a small village in the Arun District of West Sussex, England, known for its rural character and nearby railway station.
  • 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_69d6aa8550c8819095508a2ed9acf3db completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7706824d08190ba894d144cc6b3ba completed April 9, 2026, 9:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69e1554fc61c8190a0354e2f24cb62e4 completed April 16, 2026, 9:31 p.m.
Created at: April 8, 2026, 9:22 p.m.