Triple

T16858835
Position Surface form Disambiguated ID Type / Status
Subject Santiago–Rosalía de Castro Airport E409855 entity
Predicate servesAsFocusCityFor P1655 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: [Santiago–Rosalía de Castro Airport, servesAsFocusCityFor, Vueling]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vueling
Context triple: [Santiago–Rosalía de Castro Airport, servesAsFocusCityFor, 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. Crossair
    Crossair was a former Swiss regional airline that served as the main predecessor to Swiss International Air Lines after the collapse of Swissair.
  • C. Lynx Air
    Lynx Air is a Canadian ultra-low-cost airline that operates domestic and select international flights, primarily serving major hubs such as Toronto Pearson International Airport.
  • D. Interjet
    Interjet was a Mexican low-cost airline known for operating domestic and international routes across the Americas before ceasing operations in 2020.
  • E. Wizz Air
    Wizz Air is a Hungarian ultra-low-cost airline known for operating an extensive network of budget flights across Europe and surrounding regions.
  • 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_69d88395e6c88190b22730f335107c14 completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b501f72881909f7600311705fb33 completed April 18, 2026, 4:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00c2a6f2c48190874839f78f943fdb completed May 10, 2026, 5:38 p.m.
Created at: April 10, 2026, 5:24 a.m.