Triple

T22756581
Position Surface form Disambiguated ID Type / Status
Subject Peugeot 9X8 E562858 entity
Predicate team P3756 FINISHED
Object Peugeot TotalEnergies 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: Peugeot TotalEnergies | Statement: [Peugeot 9X8, team, Peugeot TotalEnergies]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peugeot TotalEnergies
Context triple: [Peugeot 9X8, team, Peugeot TotalEnergies]
  • A. Peugeot chosen
    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.
  • B. Peugeot Partner
    The Peugeot Partner is a compact panel van and leisure activity vehicle produced by the French automaker Peugeot, widely used for both commercial and family transport.
  • C. TotalEnergies
    TotalEnergies is a major French multinational energy company engaged in oil, gas, and renewable energy production and distribution worldwide.
  • D. PSA Group
    PSA Group was a major French automotive manufacturer best known for producing Peugeot, Citroën, and DS vehicles before merging to form Stellantis.
  • E. Renault
    Renault is a major French automobile manufacturer known for producing a wide range of passenger cars, commercial vehicles, and electric vehicles sold worldwide.
  • 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_69e24551ec7881909a9c924dbea155f6 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f179bd22588190ac724a656194f5b9 completed April 29, 2026, 3:23 a.m.
Created at: April 17, 2026, 3:25 p.m.