Triple

T6660083
Position Surface form Disambiguated ID Type / Status
Subject Pamplona E151451 entity
Predicate hasAirport P105 FINISHED
Object Pamplona Airport E617852 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: Pamplona Airport | Statement: [Pamplona, hasAirport, Pamplona Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pamplona Airport
Context triple: [Pamplona, hasAirport, Pamplona Airport]
  • A. Pamplona Airport chosen
    Pamplona Airport is a regional Spanish airport serving the city of Pamplona and the surrounding Navarre region with domestic and limited international flights.
  • B. Bilbao Airport
    Bilbao Airport is a major international airport in northern Spain serving the city of Bilbao and the Basque Country region.
  • C. Zaragoza Airport
    Zaragoza Airport is an international airport in northeastern Spain that serves the city of Zaragoza and functions as both a civilian and important military and cargo hub.
  • D. San Sebastián Airport
    San Sebastián Airport is a small regional airport in Spain’s Basque Country that serves the city of Donostia-San Sebastián and its surrounding area.
  • E. Burgos Airport
    Burgos Airport is a regional public airport in Burgos, Spain, providing domestic air services and connecting the city to the national air transport network.
  • 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_69c687f5fac48190a09e4838d9c6b45d completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6b071cc6c81909d7df1841c645661 completed March 27, 2026, 4:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7880545c4819091979008c84b3325 completed March 28, 2026, 7:49 a.m.
Created at: March 27, 2026, 2:02 p.m.