Triple

T15842470
Position Surface form Disambiguated ID Type / Status
Subject Benetton Group E384131 entity
Predicate engineSupplier P17328 FINISHED
Object Renault E100742 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: Renault | Statement: [Benetton Group, engineSupplier, Renault]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Renault
Context triple: [Benetton Group, engineSupplier, Renault]
  • A. Renault chosen
    Renault is a major French automobile manufacturer known for producing a wide range of passenger cars, commercial vehicles, and electric vehicles sold worldwide.
  • B. 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.
  • C. Citroën
    Citroën is a historic French automobile manufacturer known for its innovative engineering and distinctive car designs.
  • D. DS Automobiles
    DS Automobiles is a French premium automotive brand known for its avant-garde design, advanced technology, and luxury-focused vehicles.
  • E. Renault Samsung Motors
    Renault Samsung Motors is a South Korean automobile manufacturer that operates as Renault's local brand, producing and selling passenger vehicles primarily for the Korean market.
  • 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_69d86da34c888190976e06c4019d415a completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e142e88ff08190a1035269e8fdaa6a completed April 16, 2026, 8:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffb59bc1f881908c5ed93039e94a46 completed May 9, 2026, 10:30 p.m.
Created at: April 10, 2026, 4:50 a.m.