Triple

T7655424
Position Surface form Disambiguated ID Type / Status
Subject Jerez Airport E173367 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: [Jerez Airport, operator, Aena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aena
Context triple: [Jerez 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_69c6995473348190a4f41d110d619a18 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c7018ea3688190907c3ac7d25e3da6 completed March 27, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69c89afd1438819080c8f097df1d1453 completed March 29, 2026, 3:22 a.m.
Created at: March 27, 2026, 3:59 p.m.