Triple
T14796263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 16R/34L |
E347784
|
entity |
| Predicate | magneticHeading |
P54717
|
FINISHED |
| Object | approximately 160 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: approximately 160 degrees | Statement: [Runway 16R/34L, magneticHeading, approximately 160 degrees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: magneticHeading Context triple: [Runway 16R/34L, magneticHeading, approximately 160 degrees]
-
A.
hasMagneticHeading
chosen
Indicates the directional orientation of an entity relative to magnetic north, typically expressed as a magnetic compass bearing.
-
B.
hasReciprocalMagneticHeadingApprox
Indicates that two entities have magnetic headings that are approximately reciprocal (differing by about 180 degrees from each other).
-
C.
hasMagneticHeadingRangeInDegrees
Indicates the range of possible magnetic heading values, measured in degrees, associated with an entity’s orientation or navigation.
-
D.
hasApproximateHeading
Indicates that one entity’s directional heading is approximately the same as another’s, within a specified tolerance.
-
E.
azimuthAccuracy
Indicates the degree of precision or allowable error in the measured or specified azimuth angle between entities.
- 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_69d822ea8b7c819097dfadf3d45545e6 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decd5fdd548190a2ee5e668c2b20b4 |
completed | April 14, 2026, 11:27 p.m. |
| PD | Predicate disambiguation | batch_69de8c090d1081909b5a9bf437499d6c |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:31 a.m.