Triple
T25983439
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British Airways |
E646130
|
entity |
| Predicate | operatesAircraftTypeCategory |
P125702
|
FINISHED |
| Object | narrow-body jet airliners |
—
|
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 jet airliners | Statement: [British Airways, operatesAircraftTypeCategory, narrow-body jet airliners]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operatesAircraftTypeCategory Context triple: [British Airways, operatesAircraftTypeCategory, narrow-body jet airliners]
-
A.
operatesAircraftFor
Indicates that one entity pilots or controls an aircraft on behalf of, or in service to, another entity.
-
B.
aircraftTypesOperated
chosen
Indicates the types or models of aircraft that an entity (such as an airline or operator) uses or operates.
-
C.
typicalAircraftTypeCategory
Indicates the general class or category of aircraft type that is most commonly associated with or used in a given context.
-
D.
operatesAircraftBrand
Indicates that an entity actively uses or runs aircraft belonging to a specified brand in its operations.
-
E.
aircraftOperationType
Indicates the specific manner or purpose for which an aircraft is being operated (e.g., commercial, private, military, training).
- 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_69e77e881fc08190ba1c8dc7e2a07f97 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f9fd6834cc8190aa27153d6a99f3bb |
completed | May 5, 2026, 2:23 p.m. |
| PD | Predicate disambiguation | batch_69f7cf769338819092a5f42653dcc956 |
completed | May 3, 2026, 10:43 p.m. |
Created at: April 22, 2026, 8:54 a.m.