Triple
T17970400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jersey European Airways |
E449321
|
entity |
| Predicate | hadBusinessModel |
P74458
|
FINISHED |
| Object | point-to-point services |
—
|
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: point-to-point services | Statement: [Jersey European Airways, hadBusinessModel, point-to-point services]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadBusinessModel Context triple: [Jersey European Airways, hadBusinessModel, point-to-point services]
-
A.
laterBusinessModel
Indicates that one business model occurs or is adopted after another in time, representing a subsequent or successor business model in a sequence.
-
B.
businessModelWorkedOn
Indicates that an entity has actively developed, contributed to, or worked on a particular business model.
-
C.
businessModelType
chosen
Indicates the type or category of business model that characterizes how an entity creates, delivers, and captures value.
-
D.
formerBusinessModel
Indicates that an entity previously operated under a particular business model, but no longer does so.
-
E.
businessModelElement
Indicates that one entity functions as a component or element within the overall business model of another 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_69d8b9f9927c8190a006110c8b996e61 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4b1fa67c48190936f20cea45e4599 |
completed | April 19, 2026, 10:44 a.m. |
| PD | Predicate disambiguation | batch_69e3f8fa62688190a5d5c361ab896256 |
completed | April 18, 2026, 9:34 p.m. |
Created at: April 10, 2026, 10:22 a.m.