Triple
T11237723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EVA |
E265985
|
entity |
| Predicate | airlineFleetTypeOfAirline |
P87273
|
FINISHED |
| Object | Boeing 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: Boeing aircraft | Statement: [EVA, airlineFleetTypeOfAirline, Boeing aircraft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airlineFleetTypeOfAirline Context triple: [EVA, airlineFleetTypeOfAirline, Boeing aircraft]
-
A.
airlineFleetCategory
Indicates the classification category of an airline based on the characteristics or size of its aircraft fleet.
-
B.
airlineFleetDesignation
chosen
Indicates the specific fleet or operational group within an airline to which an aircraft or service is assigned.
-
C.
airlineType
Indicates the classification or category of an airline based on its operational or service characteristics.
-
D.
aircraftInFleet
Indicates that a particular aircraft is included as a member of a specified fleet.
-
E.
airlineOwnershipType
Indicates the type or nature of ownership relationship that exists between an airline and its owning entity.
- 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_69d6aac656d48190b275efaa7d6074ee |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e918375081908c2a7ccb50cbf331 |
completed | April 9, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69d7878906f48190b63ddc103a0c8f9b |
completed | April 9, 2026, 11:03 a.m. |
Created at: April 8, 2026, 9:30 p.m.