Triple

T18379754
Position Surface form Disambiguated ID Type / Status
Subject Renault Samsung Motors E446411 entity
Predicate usesTechnologyFrom P49951 FINISHED
Object Renault 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: Renault | Statement: [Renault Samsung Motors, usesTechnologyFrom, Renault]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Renault
Context triple: [Renault Samsung Motors, usesTechnologyFrom, 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 Estafette
    The Renault Estafette is a light commercial van produced by the French manufacturer Renault from the late 1950s to the early 1980s, widely used in Europe for delivery, service, and passenger 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_69d8b9f370b88190b1e5081c2c238e7f completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e51799e0f4819089e8af04888549bf completed April 19, 2026, 5:57 p.m.
Created at: April 10, 2026, 10:45 a.m.