Triple
T14229437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | dba (Deutsche BA) |
E352711
|
entity |
| Predicate | previousBusinessModel |
P5635
|
FINISHED |
| Object | full-service 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: full-service carrier | Statement: [dba (Deutsche BA), previousBusinessModel, full-service carrier]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: previousBusinessModel Context triple: [dba (Deutsche BA), previousBusinessModel, full-service carrier]
-
A.
formerBusinessModel
chosen
Indicates that an entity previously operated under a particular business model, but no longer does so.
-
B.
laterBusinessModel
Indicates that one business model occurs or is adopted after another in time, representing a subsequent or successor business model in a sequence.
-
C.
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.
-
D.
businessModelType
Indicates the type or category of business model that characterizes how an entity creates, delivers, and captures value.
-
E.
businessModelWorkedOn
Indicates that an entity has actively developed, contributed to, or worked on a particular business model.
- 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_69d8278adc7c8190a9218d69bce3c4e6 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de622b89fc8190af08dab9e1976759 |
completed | April 14, 2026, 3:50 p.m. |
| PD | Predicate disambiguation | batch_69de05bf069c8190b69f00f00f5eb126 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 1:07 a.m.