Triple

T344449
Position Surface form Disambiguated ID Type / Status
Subject easyJet E6907 entity
Predicate hasSubsidiary P254 FINISHED
Object easyJet Switzerland E6907 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: easyJet Switzerland | Statement: [easyJet, hasSubsidiary, easyJet Switzerland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: easyJet Switzerland
Context triple: [easyJet, hasSubsidiary, easyJet Switzerland]
  • A. easyJet chosen
    easyJet is a major British low-cost airline operating extensive domestic and European routes.
  • B. Ryanair
    Ryanair is a major Irish low-cost airline known for its extensive network of short-haul flights across Europe.
  • C. Air Europa
    Air Europa is a Spanish airline that operates domestic and international flights, serving as one of Spain’s major carriers and a member of the SkyTeam alliance.
  • D. Smartavia
    Smartavia is a Russian low-cost airline that operates domestic and regional flights, using Moscow Domodedovo International Airport as one of its main bases.
  • E. Vueling
    Vueling is a Spanish low-cost airline that operates extensive domestic and European routes, particularly around major hubs such as Barcelona and other key cities.
  • 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_69a2e7951ba08190960e90823b5078f3 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2eb01261c81909280128b5ce75eff completed Feb. 28, 2026, 1:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3dd2ff66c8190a0e688e4f9baa5b4 completed March 1, 2026, 6:31 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.