Triple
T7909341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Miguel District AVA |
E183657
|
entity |
| Predicate | secondaryWineStyle |
P55208
|
FINISHED |
| Object | white wine |
—
|
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: white wine | Statement: [San Miguel District AVA, secondaryWineStyle, white wine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: secondaryWineStyle Context triple: [San Miguel District AVA, secondaryWineStyle, white wine]
-
A.
secondaryWine
Indicates a relationship where one wine is designated as a secondary or supporting wine in relation to a primary wine.
-
B.
secondaryGrape
Indicates that one grape variety serves as a secondary or supporting component in a wine blend relative to the primary grape.
-
C.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
D.
wineStylesAssociatedWith
chosen
Indicates a relationship where certain wine styles are linked or connected to a particular entity, such as a region, grape, producer, or product.
-
E.
secondaryAroma
Indicates that an entity has a secondary or supporting aroma characteristic in addition to its primary scent.
- 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_69ca828dec0c81908b8f55a4dbbb53ff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a5c85ac81908de2ca387826ea2f |
completed | March 31, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69cae92f9498819085277879e59aa072 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:03 p.m.