Triple
T32975590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XY |
E843650
|
entity |
| Predicate | operatorAirlineBusinessModel |
P134019
|
FINISHED |
| Object | low-cost airline |
—
|
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: low-cost airline | Statement: [XY, operatorAirlineBusinessModel, low-cost airline]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operatorAirlineBusinessModel Context triple: [XY, operatorAirlineBusinessModel, low-cost airline]
-
A.
airlineServiceModel
chosen
Indicates a relationship where an airline operates according to, or is characterized by, a particular service model (such as full-service, low-cost, or hybrid).
-
B.
airlineOperator
Indicates that one entity operates or manages airline services for another entity or in a specified context.
-
C.
airlineContext
Indicates a relationship, situation, or action that specifically occurs within or is constrained by an airline-related context (such as flights, carriers, or air travel operations).
-
D.
airlineType
Indicates the classification or category of an airline based on its operational or service characteristics.
-
E.
representsAirline
Indicates that one entity serves as the airline associated with, operating, or branding the other entity (such as a flight, route, or service).
- 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_69f3494b9fc48190bb61c955ba471275 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a004a8892c08190bcacb952ad737716 |
completed | May 10, 2026, 9:06 a.m. |
| PD | Predicate disambiguation | batch_6a004a44b4948190be4b3dbfce8da020 |
completed | May 10, 2026, 9:05 a.m. |
Created at: May 1, 2026, 1:22 a.m.