Triple
T11774396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zimride |
E279979
|
entity |
| Predicate | businessModelElement |
P101504
|
FINISHED |
| Object | commission on rides |
—
|
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: commission on rides | Statement: [Zimride, businessModelElement, commission on rides]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: businessModelElement Context triple: [Zimride, businessModelElement, commission on rides]
-
A.
businessModelType
Indicates the type or category of business model that characterizes how an entity creates, delivers, and captures value.
-
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.
businessModelFocus
Indicates that one entity’s business model is centered on, tailored to, or primarily oriented around another entity or specific focus area.
-
D.
businessModelAdvocacy
Indicates that an entity promotes, supports, or argues in favor of a particular business model or way of generating value.
-
E.
laterBusinessModel
Indicates that one business model occurs or is adopted after another in time, representing a subsequent or successor business model in a sequence.
- F. None of above. chosen
Provenance (4 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_69d6ab01d2688190ad8ed6bda487eaa5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a8c2e8b08190a31b1e284fca2aee |
completed | April 10, 2026, 7:37 a.m. |
| PD | Predicate disambiguation | batch_69d8a242cd8c819086ed6c5f292dc8cb |
completed | April 10, 2026, 7:09 a.m. |
| PDg | Predicate description generation | batch_69d8a8c07d648190b8650d31f3a15090 |
completed | April 10, 2026, 7:37 a.m. |
Created at: April 8, 2026, 9:41 p.m.