Triple

T4497856
Position Surface form Disambiguated ID Type / Status
Subject Renault E100742 entity
Predicate notableModel P1503 FINISHED
Object Renault Master E27238 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 Master | Statement: [Renault, notableModel, Renault Master]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Renault Master
Context triple: [Renault, notableModel, Renault Master]
  • A. Renault Master chosen
    The Renault Master is a large light commercial van produced by the French manufacturer Renault, widely used in Europe for cargo and passenger transport.
  • B. Renault Kangoo
    The Renault Kangoo is a compact multi-purpose vehicle and light commercial van known for its practicality, sliding side doors, and popularity as both a family car and small business workhorse.
  • C. Citroën Jumper
    The Citroën Jumper is a large light commercial van produced by Citroën, widely used in Europe for cargo and passenger transport.
  • D. Peugeot Boxer
    The Peugeot Boxer is a large light commercial van produced by the French automaker Peugeot, widely used for cargo transport, passenger shuttles, and camper conversions.
  • E. Peugeot Expert
    The Peugeot Expert is a light commercial van produced by the French automaker Peugeot, commonly used for cargo and passenger transport in European markets.
  • 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_69bd43cdf15081909a4fa2585ff63b3e completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd56c065e88190934eb0b1632d79bb completed March 20, 2026, 2:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69bd7f6cf46c8190a4cd9075324ecc7d completed March 20, 2026, 5:10 p.m.
Created at: March 20, 2026, 1 p.m.