Triple
T27629170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stanimaka |
E696290
|
entity |
| Predicate | associatedWithWineProduction |
P201855
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Stanimaka, associatedWithWineProduction, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithWineProduction Context triple: [Stanimaka, associatedWithWineProduction, yes]
-
A.
alsoProducesWineIn
Indicates that the subject, in addition to other products or activities, produces wine in the specified location or context.
-
B.
producesWine
Indicates that one entity creates or manufactures wine as a product.
-
C.
isWineProducingTown
Indicates that a town is known for producing wine, typically through local vineyards or wineries.
-
D.
hasWinemakingFacility
Indicates that an entity possesses or is associated with a facility where winemaking activities are carried out.
-
E.
hasWineMakingTradition
Indicates that a place or group has an established, culturally recognized history and practice of producing wine.
- 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_69ef59092c8881908114ad184248cc46 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_6a002ae74da08190a4b47e0ff0f7f8fe |
completed | May 10, 2026, 6:51 a.m. |
| PD | Predicate disambiguation | batch_6a0029cc369c81909578e52c4a75ab18 |
completed | May 10, 2026, 6:46 a.m. |
| PDg | Predicate description generation | batch_6a002ae65e0c81909a5989dd73f027f5 |
completed | May 10, 2026, 6:51 a.m. |
Created at: April 27, 2026, 2:20 p.m.