Triple
T22330987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | B38M |
E552019
|
entity |
| Predicate | aircraftIcaoCategory |
P94637
|
FINISHED |
| Object | fixed-wing 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: fixed-wing aircraft | Statement: [B38M, aircraftIcaoCategory, fixed-wing aircraft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aircraftIcaoCategory Context triple: [B38M, aircraftIcaoCategory, fixed-wing aircraft]
-
A.
ICAOClassificationSystem
chosen
Indicates a relationship where an entity is categorized or defined according to the standards and categories of the ICAO (International Civil Aviation Organization) classification system.
-
B.
targetAircraftCategory
Indicates the category or type of aircraft that is the intended target of an action or operation.
-
C.
aircraftSpeedClass
Indicates the categorical speed range or performance class to which an aircraft’s speed belongs.
-
D.
aircraftRangeCategory
Indicates the classification of an aircraft based on the distance it is capable of flying on a typical mission or with standard fuel capacity.
-
E.
aircraftWakeTurbulenceCategory
Indicates the classification of an aircraft based on the strength of the wake turbulence it generates, typically used for separation and safety in air traffic control.
- 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_69e11e482f788190b78d1588fc26d606 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1577a9c348190b8662142afa832be |
completed | April 29, 2026, 12:57 a.m. |
| PD | Predicate disambiguation | batch_69e73004d9e88190bb862319a5aea06b |
completed | April 21, 2026, 8:06 a.m. |
Created at: April 16, 2026, 8:43 p.m.