Triple

T13946569
Position Surface form Disambiguated ID Type / Status
Subject Almería Airport E335399 entity
Predicate operator P179 FINISHED
Object Aena E140786 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: Aena | Statement: [Almería Airport, operator, Aena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aena
Context triple: [Almería Airport, operator, Aena]
  • A. Aena chosen
    Aena is the Spanish state-owned company that manages and operates the majority of airports in Spain and is one of the world’s largest airport operators by passenger traffic.
  • B. El Al
    El Al is Israel's flag carrier airline, known for its extensive international routes and stringent security measures.
  • C. Radazul
    Radazul is a coastal residential and marina area on the island of Tenerife in Spain’s Canary Islands.
  • D. Iberia Líneas Aéreas de España
    Iberia Líneas Aéreas de España is the flag carrier airline of Spain, operating an extensive network of domestic and international flights, primarily through its main hub in Madrid.
  • 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_69d81c6081b88190b53e317c3370c8fe completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2e10f60c81908ee9636e85c070ff completed April 14, 2026, 12:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7ce903c5c8190b72d83a5b842ad70 completed May 3, 2026, 10:39 p.m.
Created at: April 9, 2026, 10:17 p.m.