Triple

T76735
Position Surface form Disambiguated ID Type / Status
Subject Fiat Lux E1532 entity
Predicate hasWord P35 FINISHED
Object Fiat E2758 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: Fiat | Statement: [Fiat Lux, hasWord, Fiat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fiat
Context triple: [Fiat Lux, hasWord, Fiat]
  • A. Stellantis chosen
    Stellantis is a multinational automotive manufacturing corporation formed through the merger of Fiat Chrysler Automobiles and PSA Group, producing a wide range of vehicles under brands such as Jeep, Peugeot, Citroën, and Fiat.
  • B. Saab Automobile
    Saab Automobile was a Swedish car manufacturer known for its innovative engineering, turbocharged engines, and distinctive, safety-focused designs.
  • C. Lotus
    The lotus is a sacred aquatic flower widely revered in Indian culture and religion, symbolizing purity, beauty, and spiritual enlightenment.
  • D. Volkswagen Group
    Volkswagen Group is a major German multinational automotive manufacturer that owns brands such as Volkswagen, Audi, Porsche, and Škoda and is one of the largest car producers in the world.
  • E. Packard
    Packard is a surname most prominently associated with David Packard, the American electrical engineer and co-founder of Hewlett-Packard.
  • 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_69a24c60d19c8190a1b6c105ca59ef5b completed Feb. 28, 2026, 2:01 a.m.
NER Named-entity recognition batch_69a2567c90308190a9b989c586f7e559 completed Feb. 28, 2026, 2:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69a266ea53f48190a0a09700697ffd45 completed Feb. 28, 2026, 3:54 a.m.
Created at: Feb. 28, 2026, 2:06 a.m.