Triple
T33033197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duckhorn Vineyards |
E845224
|
entity |
| Predicate | hasWinemakingFocus |
P180369
|
FINISHED |
| Object | estate-grown fruit |
—
|
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: estate-grown fruit | Statement: [Duckhorn Vineyards, hasWinemakingFocus, estate-grown fruit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWinemakingFocus Context triple: [Duckhorn Vineyards, hasWinemakingFocus, estate-grown fruit]
-
A.
hasWinemakingFacility
Indicates that an entity possesses or is associated with a facility where winemaking activities are carried out.
-
B.
hasWinemakingOption
Indicates that an entity offers or includes a particular method, feature, or configuration related to winemaking.
-
C.
hasWineMakingTradition
Indicates that a place or group has an established, culturally recognized history and practice of producing wine.
-
D.
producesWine
Indicates that one entity creates or manufactures wine as a product.
-
E.
hasWinery
Indicates a relationship where a subject owns, operates, or is associated with a particular winery.
- 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_69f34951348c8190b56746b0a7018182 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f73ae120bc8190bff94d38d7a7a00d |
completed | May 3, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69f73a38d0848190aa5139144b8561c6 |
completed | May 3, 2026, 12:06 p.m. |
| PDg | Predicate description generation | batch_69f73adfd9a081908adae6bd59dfefb9 |
completed | May 3, 2026, 12:09 p.m. |
Created at: May 1, 2026, 1:24 a.m.