Triple
T1890029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amakusa Airlines |
E41851
|
entity |
| Predicate | fleetCharacteristic |
P10916
|
FINISHED |
| Object | turboprop 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: turboprop aircraft | Statement: [Amakusa Airlines, fleetCharacteristic, turboprop aircraft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fleetCharacteristic Context triple: [Amakusa Airlines, fleetCharacteristic, turboprop aircraft]
-
A.
militaryCharacteristic
Indicates that one entity possesses a specific military-related attribute, quality, or feature in relation to another entity or context.
-
B.
fleetIncludes
chosen
Indicates that a particular fleet contains or is composed of the specified entity or entities as its members.
-
C.
logisticalCharacteristic
Indicates the logistical properties or constraints associated with an entity, such as how it is stored, transported, handled, or supplied.
-
D.
equipmentCharacteristic
Indicates that a specific characteristic, property, or attribute is associated with a piece of equipment.
-
E.
transportCharacteristic
Indicates a relationship where a specific characteristic, property, or feature is attributed to a mode or instance of transport.
- 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_69a8864b6de0819098d089f6a1b910a7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb142e41881908fc7335673a9dec3 |
completed | March 7, 2026, 5:01 a.m. |
| PD | Predicate disambiguation | batch_69abafe61bc48190ac9ead027df930e1 |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:34 p.m.