Triple
T4113573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 8L/26R |
E90234
|
entity |
| Predicate | hasMagneticHeading |
P54717
|
FINISHED |
| Object | approximately 080 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 080 degrees | Statement: [Runway 8L/26R, hasMagneticHeading, approximately 080 degrees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMagneticHeading Context triple: [Runway 8L/26R, hasMagneticHeading, approximately 080 degrees]
-
A.
hasReciprocalMagneticHeadingApprox
Indicates that two entities have magnetic headings that are approximately reciprocal (differing by about 180 degrees from each other).
-
B.
hasMagneticMoment
Indicates that an entity possesses a magnetic moment, characterizing the strength and orientation of its magnetism.
-
C.
hasRelationToTrueHeading
Indicates that an entity has a specified relationship or correspondence to a true (reference) heading or direction.
-
D.
magneticField
Indicates the presence, strength, or configuration of a magnetic field associated with an entity or region.
-
E.
usesMagnetType
Indicates that one entity employs or operates with a specific type of magnet in its function or configuration.
- F. None of above. chosen
Provenance (4 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_69aed95c080881908125e30c5dcdc6f8 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af03d7240c8190a64dcbc669772808 |
completed | March 9, 2026, 5:31 p.m. |
| PD | Predicate disambiguation | batch_69af0183eb84819087d7184de28f5514 |
completed | March 9, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69af03d5cce88190ad53bc6cfe10b21e |
completed | March 9, 2026, 5:31 p.m. |
Created at: March 9, 2026, 3:41 p.m.