Triple

T34895621
Position Surface form Disambiguated ID Type / Status
Subject air transport via Caucayá Airport E1006425 entity
Predicate operatesFromRunwaySurface P422 FINISHED
Object asphalt LITERAL 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: asphalt | Statement: [air transport via Caucayá Airport, operatesFromRunwaySurface, asphalt]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: operatesFromRunwaySurface
Context triple: [air transport via Caucayá Airport, operatesFromRunwaySurface, asphalt]
  • A. runwaySurface chosen
    Indicates the type or condition of the surface material that a runway is made of or covered with.
  • B. isCodeForRunwayOperationsAt
    Indicates that a given code designates or is used to identify runway operations at a specific location or airport.
  • C. runwayPerformance
    Indicates the performance characteristics or behavior of an entity (such as an aircraft or vehicle) when operating on a runway, including factors like acceleration, deceleration, and required distances.
  • D. runwayLength
    Indicates the length of a runway associated with an airport or airfield.
  • E. hasRunwayOperations
    Indicates that an entity conducts or is involved in operational activities on an airport runway, such as takeoffs, landings, or related ground movements.
  • F. None of above.

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_69f76dbfe5788190ad8b64f241f470c8 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_6a01185c46f0819089b4a2ad3c3e2f33 completed May 10, 2026, 11:44 p.m.
PD Predicate disambiguation batch_6a0117e19e008190870663dd45084416 completed May 10, 2026, 11:42 p.m.
Created at: May 3, 2026, 4 p.m.