Triple

T5566640
Position Surface form Disambiguated ID Type / Status
Subject Renault Super Goélette E145895 entity
Predicate brand P1500 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: [Renault Super Goélette, brand, Renault]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Renault
Context triple: [Renault Super Goélette, brand, 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_69c008fdae24819081aa002ad99cd966 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c02034fc3081908920c52a19d462e1 completed March 22, 2026, 5 p.m.
NED1 Entity disambiguation (via context triple) batch_69c059f006e081908c332f0470f38374 completed March 22, 2026, 9:06 p.m.
Created at: March 22, 2026, 3:36 p.m.