Triple
T10181640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 14R/32L |
E236798
|
entity |
| Predicate | runwayNumberingSystem |
P55035
|
FINISHED |
| Object | ICAO standard magnetic heading-based numbering |
—
|
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: ICAO standard magnetic heading-based numbering | Statement: [Runway 14R/32L, runwayNumberingSystem, ICAO standard magnetic heading-based numbering]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runwayNumberingSystem Context triple: [Runway 14R/32L, runwayNumberingSystem, ICAO standard magnetic heading-based numbering]
-
A.
usesRunwayNumberingConvention
chosen
Indicates that an airport or runway follows a specific standardized system for assigning runway identification numbers.
-
B.
hasRunwayNumber
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.
runwayCharacteristic
Indicates a relationship where specific attributes or features are associated with a runway.
-
E.
numberOfRunways
Indicates the quantity of runways associated with a given entity, such as an airport or airfield.
- 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_69ca84d7260c8190bfbec36762943f37 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cded32b91c8190b01ad37b2456080a |
completed | April 2, 2026, 4:14 a.m. |
| PD | Predicate disambiguation | batch_69cd7c79f21c8190a7f31b2eab80b8ba |
completed | April 1, 2026, 8:13 p.m. |
Created at: March 30, 2026, 9:12 p.m.