Triple
T666034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 07R/25L |
E12862
|
entity |
| Predicate | supportsAircraftCategory |
P5536
|
FINISHED |
| Object | large commercial jet aircraft |
—
|
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: large commercial jet aircraft | Statement: [Runway 07R/25L, supportsAircraftCategory, large commercial jet aircraft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsAircraftCategory Context triple: [Runway 07R/25L, supportsAircraftCategory, large commercial jet aircraft]
-
A.
supportsNarrowBodyAircraft
Indicates that one entity is capable of accommodating, servicing, or being compatible with narrow-body aircraft.
-
B.
supportsWideBodyAircraft
chosen
Indicates that an entity (such as an airport, runway, or gate) is capable of accommodating and handling wide-body aircraft operations.
-
C.
aircraftType
Indicates the specific model or category of aircraft associated with an entity or event.
-
D.
aircraftTypesCarried
Indicates that one entity (typically a vessel, facility, or platform) carries or is capable of carrying specific types of aircraft as part of its operations or configuration.
-
E.
usedOnAircraftName
Indicates that something is employed or applied on an aircraft identified by a specific name.
- 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_69a493355dec819098d4244b2fa34885 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49fd4f4988190a0973ceb7329b4c9 |
completed | March 1, 2026, 8:21 p.m. |
| PD | Predicate disambiguation | batch_69a49d16cff881908c8d2c3fe4d1d6fb |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.