Triple

T1345311
Position Surface form Disambiguated ID Type / Status
Subject Vueling E28556 entity
Predicate callsign P1565 FINISHED
Object VUELING E28556 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: VUELING | Statement: [Vueling, callsign, VUELING]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: VUELING
Context triple: [Vueling, callsign, VUELING]
  • A. Vueling chosen
    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.
  • B. Luft
    Luft is a surname most notably associated with Sid Luft, the American film producer and third husband of entertainer Judy Garland.
  • C. Volaris
    Volaris is a Mexican low-cost airline that operates domestic and international flights, primarily serving routes across Mexico, the United States, and Central America.
  • D. Flying J
    Flying J is a chain of highway travel centers and truck stops in North America, known for providing fuel, food, and amenities for professional drivers and motorists.
  • E. 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.
  • 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_69a49854eb3481908c7d56b2e449a290 completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c23d696c8190bb688274280cb680 completed March 1, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69acc6351cbc81909e2ffc692ee92b54 completed March 8, 2026, 12:43 a.m.
Created at: March 1, 2026, 7:56 p.m.