Triple
T7380693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 15/33 |
E170238
|
entity |
| Predicate | runwayNumberingBasedOn |
P8866
|
FINISHED |
| Object | magnetic heading |
—
|
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: magnetic heading | Statement: [Runway 15/33, runwayNumberingBasedOn, magnetic heading]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runwayNumberingBasedOn Context triple: [Runway 15/33, runwayNumberingBasedOn, magnetic heading]
-
A.
usesRunwayNumberingConvention
Indicates that an airport or runway follows a specific standardized system for assigning runway identification numbers.
-
B.
hasRunwayNumber
chosen
Indicates that an airport or airfield runway is assigned a specific identifying number.
-
C.
runwayFormat
Indicates the specific physical configuration or layout type of a runway used for takeoff and landing.
-
D.
hasRunwayDesignationSide
Indicates that a runway designation is associated with a specific side or direction of the runway (e.g., left, right, or center).
-
E.
runwayCharacteristic
Indicates a relationship where specific attributes or features are associated with a 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_69c68a5d0ed08190b6d361e68f813330 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1c7b5bc81908afa2bf39159979b |
completed | March 27, 2026, 9:08 p.m. |
| PD | Predicate disambiguation | batch_69c6f02ee3e08190a7a00c981129b22c |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:08 p.m.