Triple
T4304956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 10R/28L |
E99931
|
entity |
| Predicate | usesRunwayNumberingConvention |
P55035
|
FINISHED |
| Object | magnetic heading divided by 10 and rounded |
—
|
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 divided by 10 and rounded | Statement: [Runway 10R/28L, usesRunwayNumberingConvention, magnetic heading divided by 10 and rounded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesRunwayNumberingConvention Context triple: [Runway 10R/28L, usesRunwayNumberingConvention, magnetic heading divided by 10 and rounded]
-
A.
hasRunwayNumber
Indicates that an airport or airfield runway is assigned a specific identifying number.
-
B.
runwayFormat
Indicates the specific physical configuration or layout type of a runway used for takeoff and landing.
-
C.
usesRunwayOf
Indicates that one entity makes use of the runway that belongs to or is associated with another entity.
-
D.
hasRunwayMarkings
Indicates that a runway possesses specific painted markings or symbols on its surface.
-
E.
hasRunwayCount
Indicates the number of runways that a given entity (such as an airport) possesses.
- 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_69b345528ebc8190b5abc7e95094792d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b350b8e1cc819094ce3d6f6c8da767 |
completed | March 12, 2026, 11:48 p.m. |
| PD | Predicate disambiguation | batch_69b347ff45cc8190b0cc335a94cc3d73 |
completed | March 12, 2026, 11:10 p.m. |
| PDg | Predicate description generation | batch_69b34e06b3ec81909298b1ddd74d37bd |
completed | March 12, 2026, 11:36 p.m. |
Created at: March 12, 2026, 11:09 p.m.