Triple

T20158849
Position Surface form Disambiguated ID Type / Status
Subject VAL 206 E491648 entity
Predicate marketedBy P4613 FINISHED
Object Matra Transport NE NERFINISHED

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: Matra Transport | Statement: [VAL 206, marketedBy, Matra Transport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Matra Transport
Context triple: [VAL 206, marketedBy, Matra Transport]
  • A. Matra Transport chosen
    Matra Transport is a French company known for designing and producing automated urban transit and people-mover systems used in cities and airports worldwide.
  • B. Matra Automobiles
    Matra Automobiles was a French car manufacturer best known for its innovative sports cars and collaborations with brands like Renault and Simca during the late 20th century.
  • C. Traton
    Traton is a commercial vehicle manufacturer and holding company that oversees brands like MAN and Scania within the Volkswagen Group.
  • D. Peugeot
    Peugeot is a historic French automobile manufacturer known for producing a wide range of passenger cars and commercial vehicles, now operating as a core brand within the multinational automotive group Stellantis.
  • E. Renault Trucks
    Renault Trucks is a French commercial vehicle manufacturer known for producing a wide range of trucks and heavy-duty vehicles for distribution, construction, and long-haul transport.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da6266c6888190bc1a3ecf24814d34 completed April 11, 2026, 3:01 p.m.
NER Named-entity recognition batch_69e667e27aa88190a326288b992ea274 completed April 20, 2026, 5:52 p.m.
Created at: April 11, 2026, 11:34 p.m.