Triple

T36626456
Position Surface form Disambiguated ID Type / Status
Subject SLF Runway 15 E904180 entity
Predicate hasOppositeRunwayDesignation P66860 FINISHED
Object 33 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: 33 | Statement: [SLF Runway 15, hasOppositeRunwayDesignation, 33]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasOppositeRunwayDesignation
Context triple: [SLF Runway 15, hasOppositeRunwayDesignation, 33]
  • A. hasOppositeRunway chosen
    Indicates that one runway is paired with another runway that has the opposite or reciprocal orientation or designation.
  • B. hasRunwayDesignationSide
    Indicates that a runway designation is associated with a specific side or direction of the runway (e.g., left, right, or center).
  • C. hasParallelRunwayIndicator
    Indicates that one runway serves as a parallel counterpart or reference indicator for another runway within an airport or airfield.
  • D. hasParallelRunwaySystemRole
    Indicates that an entity holds a specific role or function within a parallel runway system.
  • E. hasParallelRunway
    Indicates that one runway is parallel in orientation and alignment to another runway.
  • 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_69f76e6ae750819096911e6e2d4d12c5 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69fd37b695c88190855801626f91c4cd completed May 8, 2026, 1:09 a.m.
PD Predicate disambiguation batch_69fd374cccf08190a230e87164af5938 completed May 8, 2026, 1:07 a.m.
Created at: May 3, 2026, 4:11 p.m.