Triple
T25220917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gwen Schantz |
E631958
|
entity |
| Predicate | businessModelInvolves |
P101504
|
FINISHED |
| Object | wholesale produce sales |
—
|
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: wholesale produce sales | Statement: [Gwen Schantz, businessModelInvolves, wholesale produce sales]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: businessModelInvolves Context triple: [Gwen Schantz, businessModelInvolves, wholesale produce sales]
-
A.
businessModelType
Indicates the type or category of business model that characterizes how an entity creates, delivers, and captures value.
-
B.
businessModelElement
chosen
Indicates that one entity functions as a component or element within the overall business model of another entity.
-
C.
businessModelWorkedOn
Indicates that an entity has actively developed, contributed to, or worked on a particular business model.
-
D.
businessModelFocus
Indicates that one entity’s business model is centered on, tailored to, or primarily oriented around another entity or specific focus area.
-
E.
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.
- 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_69e75a8e0f688190a7aebe9a4815e25b |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f47cbe66648190a46a29d187ab2c47 |
completed | May 1, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69f45d06d0388190b36ecde92013624a |
completed | May 1, 2026, 7:57 a.m. |
Created at: April 21, 2026, 1:03 p.m.