Triple
T24177851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turkish Cypriot airlines |
E599332
|
entity |
| Predicate | typicalAircraftUse |
P45618
|
FINISHED |
| Object | narrow-body jets |
—
|
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: narrow-body jets | Statement: [Turkish Cypriot airlines, typicalAircraftUse, narrow-body jets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAircraftUse Context triple: [Turkish Cypriot airlines, typicalAircraftUse, narrow-body jets]
-
A.
typicalAircraftTypeCategory
chosen
Indicates the general class or category of aircraft type that is most commonly associated with or used in a given context.
-
B.
aviationUsage
Indicates that something is used for, involved in, or associated with aviation-related activities or purposes.
-
C.
aircraftUsers
Indicates that one entity uses, operates, or employs an aircraft associated with another entity.
-
D.
usedByAircraftType
Indicates that something (such as equipment, infrastructure, or a procedure) is employed or operated by a specific type or category of aircraft.
-
E.
theaterOfUseOfAircraft
Indicates the geographic or operational area in which an aircraft is intended to be or is actually employed.
- 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_69e288cca05481908faeb1563711114a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f27c9ddfcc819096697a844b300cce |
completed | April 29, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f1c42f942c8190b103ff29a60fef34 |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 17, 2026, 11:34 p.m.