Triple
T6562995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Curicó Valley wine region |
E153831
|
entity |
| Predicate | typicalSauvignonBlancProfile |
P16142
|
FINISHED |
| Object | fresh and aromatic |
—
|
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: fresh and aromatic | Statement: [Curicó Valley wine region, typicalSauvignonBlancProfile, fresh and aromatic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSauvignonBlancProfile Context triple: [Curicó Valley wine region, typicalSauvignonBlancProfile, fresh and aromatic]
-
A.
typicalViognierPercentage
Indicates the usual proportion of Viognier used within a given wine, blend, or production context.
-
B.
wineCharacteristic
chosen
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
C.
wineAcidityType
Indicates the type or category of acidity associated with a given wine.
-
D.
whiteWineShare
Indicates the proportion or share of white wine within a larger set, such as total wine consumption, production, or sales.
-
E.
primaryGrapeVariety
Indicates that one entity is the main or predominant grape variety used in producing the other entity (typically a wine or wine-based product).
- 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_69c6880cb35881909b763eb0125236b9 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6c1b15d3481908ae66e3d7564b352 |
completed | March 27, 2026, 5:43 p.m. |
| PD | Predicate disambiguation | batch_69c6acf6d4148190914b19e9affd8c76 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:52 p.m.