Triple

T1319113
Position Surface form Disambiguated ID Type / Status
Subject Barcelona El Prat Airport E28174 entity
Predicate owner P347 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: [Barcelona El Prat Airport, owner, Aena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aena
Context triple: [Barcelona El Prat Airport, owner, 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. 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.
  • D. Valencia Airport
    Valencia Airport is an international airport serving the city of Valencia and the surrounding region on Spain’s eastern Mediterranean coast.
  • E. La Palma Airport
    La Palma Airport is the main commercial airport serving the island of La Palma in Spain’s Canary Islands, handling domestic and limited international flights for residents and tourists.
  • 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_69a498532c3481909223b74af2e578df completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c1780be8819083a9365b8a49305d completed March 1, 2026, 10:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbf2b8d988190b0d84e886629ed05 completed March 8, 2026, 12:13 a.m.
Created at: March 1, 2026, 7:55 p.m.