Triple
T32951652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 9L/27R |
E842971
|
entity |
| Predicate | hasRightRunwayOnHeading |
P6272
|
FINISHED |
| Object | 270 degrees |
—
|
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: 270 degrees | Statement: [Runway 9L/27R, hasRightRunwayOnHeading, 270 degrees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRightRunwayOnHeading Context triple: [Runway 9L/27R, hasRightRunwayOnHeading, 270 degrees]
-
A.
hasRightRunwayIn25Direction
Indicates that an entity (typically an airport or airfield) possesses a right-hand runway aligned or oriented in the 25 direction.
-
B.
hasLeftRunwayIn07Direction
Indicates that an aircraft has departed or exited the runway heading in the 07 (070°) direction.
-
C.
hasRunwayOrientation
chosen
Indicates that a runway is aligned or oriented in a specific directional heading.
-
D.
hasRunwayApproachType
Indicates the specific type or category of approach procedure associated with a runway.
-
E.
hasOppositeRunway
Indicates that one runway is paired with another runway that has the opposite or reciprocal orientation or designation.
- 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_69f3494a31f481909057136e49b4fe60 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f7c777e924819081a6634f549fe552 |
completed | May 3, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69f7c475c58c8190a883554231e88c88 |
completed | May 3, 2026, 9:56 p.m. |
Created at: May 1, 2026, 1:21 a.m.