Triple

T1345328
Position Surface form Disambiguated ID Type / Status
Subject Vueling E28556 entity
Predicate focusCity P164 FINISHED
Object Bilbao Airport E141200 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: Bilbao Airport | Statement: [Vueling, focusCity, Bilbao Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bilbao Airport
Context triple: [Vueling, focusCity, Bilbao Airport]
  • A. Bilbao Airport chosen
    Bilbao Airport is a major international airport in northern Spain serving the city of Bilbao and the Basque Country region.
  • B. Valladolid Airport
    Valladolid Airport is a regional Spanish airport serving the city of Valladolid and the surrounding Castile and León region, handling domestic and limited international flights.
  • C. Valencia Airport
    Valencia Airport is an international airport serving the city of Valencia and the surrounding region on Spain’s eastern Mediterranean coast.
  • D. Seville Airport
    Seville Airport is a major international and military aviation hub in southern Spain that serves the city of Seville and hosts significant aircraft manufacturing and testing activities.
  • E. Málaga Airport
    Málaga Airport is a major international airport in southern Spain serving the Costa del Sol and the city of Málaga as one of the country’s busiest tourist gateways.
  • 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_69ad08a7ed5c8190b9f99a6f4524eae8 completed March 8, 2026, 5:27 a.m.
Created at: March 1, 2026, 7:56 p.m.