Triple

T2358261
Position Surface form Disambiguated ID Type / Status
Subject Mombasa E47207 entity
Predicate hasAirport P105 FINISHED
Object Moi International Airport E167893 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: Moi International Airport | Statement: [Mombasa, hasAirport, Moi International Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Moi International Airport
Context triple: [Mombasa, hasAirport, Moi International Airport]
  • A. Moi International Airport chosen
    Moi International Airport is a major international airport serving the coastal city of Mombasa, Kenya, handling both domestic and international passenger and cargo flights.
  • B. Léon-Mba International Airport
    Léon-Mba International Airport is the main international gateway to Gabon, serving the capital city of Libreville with both domestic and international flights.
  • C. Benina International Airport
    Benina International Airport is the main international airport serving the city of Benghazi in eastern Libya.
  • D. Mpanda Airport
    Mpanda Airport is a regional airport in western Tanzania that serves the town of Mpanda and the surrounding Katavi Region.
  • E. Mitiga International Airport
    Mitiga International Airport is a major airport serving Tripoli, Libya, handling both domestic and international flights.
  • 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_69a88a1a4a6081908645b0f2914521ab completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abc71f767481908dfa9be209ea3c5a completed March 7, 2026, 6:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69aeb3c37c448190b2d1fd5c404e0050 completed March 9, 2026, 11:49 a.m.
Created at: March 4, 2026, 7:55 p.m.