Triple

T4495953
Position Surface form Disambiguated ID Type / Status
Subject JetSMART E100695 entity
Predicate hasSubsidiary P254 FINISHED
Object JetSMART Argentina E100695 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: JetSMART Argentina | Statement: [JetSMART, hasSubsidiary, JetSMART Argentina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: JetSMART Argentina
Context triple: [JetSMART, hasSubsidiary, JetSMART Argentina]
  • A. JetSMART chosen
    JetSMART is a Chilean low-cost airline operating domestic and regional flights across South America.
  • B. JetSmart Perú
    JetSmart Perú is a Peruvian low-cost airline operating domestic and regional flights as part of the South American JetSmart network.
  • C. Sky Airline
    Sky Airline is a Chilean low-cost carrier that operates domestic and regional flights across South America.
  • D. Aerolineas Argentinas
    Aerolíneas Argentinas is the flag carrier airline of Argentina, operating domestic and international flights primarily from its hub in Buenos Aires.
  • E. Viva Aerobus
    Viva Aerobus is a Mexican low-cost airline known for offering budget-friendly domestic and regional flights across Mexico and select international destinations.
  • 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_69bd56bde14c819091d42839a46291d0 completed March 20, 2026, 2:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69bd67bfb4788190b64975b1999a8d1e completed March 20, 2026, 3:29 p.m.
Created at: March 20, 2026, 1 p.m.