Triple

T6281301
Position Surface form Disambiguated ID Type / Status
Subject Aena E140786 entity
Predicate operates P24 FINISHED
Object Melilla Airport E152104 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: Melilla Airport | Statement: [Aena, operates, Melilla Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Melilla Airport
Context triple: [Aena, operates, Melilla Airport]
  • A. Melilla Airport chosen
    Melilla Airport is a small regional airport in the Spanish autonomous city of Melilla on the north coast of Africa, providing domestic connections to mainland Spain.
  • B. Almería Airport
    Almería Airport is a regional international airport in southeastern Spain that serves the city of Almería and the surrounding Costa de Almería tourist area.
  • C. 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.
  • D. Tangier Ibn Battouta Airport
    Tangier Ibn Battouta Airport is an international airport serving the city of Tangier in northern Morocco, named after the famous Moroccan explorer Ibn Battuta.
  • E. Zagora Airport
    Zagora Airport is a small regional airport in southeastern Morocco that serves as a gateway for travelers heading into the nearby Sahara Desert and surrounding desert attractions.
  • 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_69c008cd17c8819082b82d3fbeb68047 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c063dee62881908347283f16dcbe68 completed March 22, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69c51962132881909a2eccd1203e03c1 completed March 26, 2026, 11:32 a.m.
Created at: March 22, 2026, 4:26 p.m.