Triple
T6296991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 09L/27R |
E141153
|
entity |
| Predicate | hasRunwayNumberingConvention |
P55035
|
FINISHED |
| Object | 09L for eastbound operations |
—
|
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: 09L for eastbound operations | Statement: [Runway 09L/27R, hasRunwayNumberingConvention, 09L for eastbound operations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRunwayNumberingConvention Context triple: [Runway 09L/27R, hasRunwayNumberingConvention, 09L for eastbound operations]
-
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.
hasRunwayDesignationSide
Indicates that a runway designation is associated with a specific side or direction of the runway (e.g., left, right, or center).
-
D.
hasRunwayCount
Indicates the number of runways that a given entity (such as an airport) possesses.
-
E.
hasRunwayMarkings
Indicates that a runway possesses specific painted markings or symbols on its surface.
- 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_69c008cdf2ac8190bb640c94478fb4ed |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0643ac2b48190b2db036ce709e7ea |
completed | March 22, 2026, 9:50 p.m. |
| PD | Predicate disambiguation | batch_69c060df0d8881908215575862ef6831 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:27 p.m.