Triple

T1903552
Position Surface form Disambiguated ID Type / Status
Subject SEAT E37746 entity
Predicate brand P1500 FINISHED
Object SEAT E37746 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: SEAT | Statement: [SEAT, brand, SEAT]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SEAT
Context triple: [SEAT, brand, SEAT]
  • A. SEAT chosen
    SEAT is a Spanish automobile manufacturer known for producing affordable, stylish cars and operating as a subsidiary of the Volkswagen Group.
  • B. SEAT León
    The SEAT León is a compact hatchback car produced by Spanish manufacturer SEAT, known for combining sporty styling and performance with everyday practicality.
  • C. SEAT Ibiza
    The SEAT Ibiza is a popular supermini car produced by Spanish automaker SEAT, known for its compact size, practicality, and value-oriented positioning in the European market.
  • D. 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.
  • E. Lancia
    Lancia is an Italian automobile manufacturer renowned for its historic innovations and success in motorsport, particularly rally racing.
  • 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_69a8861be7148190a680937ec451a304 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb1909aec8190b3259c8f969ce81e completed March 7, 2026, 5:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3d0d01c8190ae0c8029fead4008 completed March 8, 2026, 10:10 p.m.
Created at: March 4, 2026, 7:35 p.m.