Triple
T2251205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Air Hong Kong |
E49619
|
entity |
| Predicate | mainBusinessModel |
P21224
|
FINISHED |
| Object | express freight |
—
|
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: express freight | Statement: [Air Hong Kong, mainBusinessModel, express freight]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainBusinessModel Context triple: [Air Hong Kong, mainBusinessModel, express freight]
-
A.
businessModelFocus
chosen
Indicates that one entity’s business model is centered on, tailored to, or primarily oriented around another entity or specific focus area.
-
B.
businessModelPioneerOf
Indicates that an entity was the first or among the first to introduce, develop, or popularize a particular business model that others later adopted.
-
C.
formerBusinessModel
Indicates that an entity previously operated under a particular business model, but no longer does so.
-
D.
operatingModel
Indicates how an organization structures and manages its processes, resources, and governance to deliver its products or services.
-
E.
usesBusinessModel
Indicates that one entity operates according to, or applies in practice, the business model defined or provided by 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_69a88aaa9250819095e127d0d77e8a32 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc11d04688190abc04fac3a1804a9 |
completed | March 7, 2026, 6:09 a.m. |
| PD | Predicate disambiguation | batch_69abbdb160248190aa75b38f11ad8602 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:47 p.m.