Triple

T273614
Position Surface form Disambiguated ID Type / Status
Subject KBWI E5198 entity
Predicate hasRunway P105 FINISHED
Object Runway 15R/33L E8391 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: Runway 15R/33L | Statement: [KBWI, hasRunway, Runway 15R/33L]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Runway 15R/33L
Context triple: [KBWI, hasRunway, Runway 15R/33L]
  • A. Runway 15L/33R chosen
    Runway 15L/33R is one of the primary paved runways at Baltimore/Washington International Thurgood Marshall Airport, used for handling commercial air traffic in both directions.
  • B. Runway 10L/28R
    Runway 10L/28R is one of the primary parallel runways at San Francisco International Airport, used extensively for both arrivals and departures.
  • C. Runway 15/33
    Runway 15/33 is one of the primary runways at Ronald Reagan Washington National Airport in Arlington, Virginia, serving commercial air traffic for the Washington, D.C. area.
  • D. Runway 10/28
    Runway 10/28 is a principal paved runway at José Martí International Airport in Havana, Cuba, used for handling a significant share of the airport’s commercial air traffic.
  • E. Runway 05L/23R
    Runway 05L/23R is one of the main parallel runways at Manchester Airport, used for handling a high volume of commercial air traffic.
  • 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_69a257e6c8788190987dfe705ca2912a completed Feb. 28, 2026, 2:50 a.m.
NER Named-entity recognition batch_69a25dd0a99c819089968a5400c58c5f completed Feb. 28, 2026, 3:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69a38f537f1c8190a59ac4669498a3fc completed March 1, 2026, 12:58 a.m.
Created at: Feb. 28, 2026, 2:59 a.m.