Triple
T27454703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 3L |
E692554
|
entity |
| Predicate | hasRunwayThresholdDirection |
P6272
|
FINISHED |
| Object | 030 degrees magnetic |
—
|
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: 030 degrees magnetic | Statement: [Runway 3L, hasRunwayThresholdDirection, 030 degrees magnetic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRunwayThresholdDirection Context triple: [Runway 3L, hasRunwayThresholdDirection, 030 degrees magnetic]
-
A.
hasRunwayOrientation
chosen
Indicates that a runway is aligned or oriented in a specific directional heading.
-
B.
hasRunwayPosition
Indicates the spatial or designated placement of an aircraft or object relative to a specific runway.
-
C.
hasRunwayApproachType
Indicates the specific type or category of approach procedure associated with a runway.
-
D.
hasRunwayConfiguration
Indicates a specific arrangement or setup of runways associated with an airport, airfield, or similar facility.
-
E.
hasRunwayPresence
Indicates that an entity maintains a physical runway or landing strip suitable for aircraft operations.
- 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_69ef5207903881909427745cda05d27a |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f65f7731e4819099d5bd3d915ee266 |
completed | May 2, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f65c2198208190a3954086c22cfcbf |
completed | May 2, 2026, 8:18 p.m. |
Created at: April 27, 2026, 12:48 p.m.