Triple
T13215981
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nice (Breeze Airways cabin) |
E314616
|
entity |
| Predicate | airlineBusinessModelContext |
P15154
|
FINISHED |
| Object | low-cost carrier |
—
|
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 carrier | Statement: [Nice (Breeze Airways cabin), airlineBusinessModelContext, low-cost carrier]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airlineBusinessModelContext Context triple: [Nice (Breeze Airways cabin), airlineBusinessModelContext, low-cost carrier]
-
A.
airlineAllianceContext
Indicates the contextual relationship between an airline and its membership or participation status within a specific airline alliance.
-
B.
airlineType
chosen
Indicates the classification or category of an airline based on its operational or service characteristics.
-
C.
airlineCategory
Indicates the classification or type of an airline within a defined categorization system (e.g., full-service, low-cost, regional).
-
D.
mainAirlineFocus
Indicates that an airline is the primary or central focus of attention, operations, or analysis in a given context.
-
E.
airlineMarket
Indicates a commercial air transport relationship where an airline provides or operates flight services within a specific origin–destination market or route.
- 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_69d806aee7308190b70a237ba2a6e3e1 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98cf28c9c819080d7b42d20f579d1 |
completed | April 10, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69d98bc938f081909f123bdf1263ff7f |
completed | April 10, 2026, 11:46 p.m. |
Created at: April 9, 2026, 9:18 p.m.