Triple
T6562994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Curicó Valley wine region |
E153831
|
entity |
| Predicate | typicalCabernetProfile |
P69349
|
FINISHED |
| Object | ripe fruit and soft tannins |
—
|
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: ripe fruit and soft tannins | Statement: [Curicó Valley wine region, typicalCabernetProfile, ripe fruit and soft tannins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCabernetProfile Context triple: [Curicó Valley wine region, typicalCabernetProfile, ripe fruit and soft tannins]
-
A.
wineCharacteristic
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
B.
hasTastingProfile
chosen
Indicates that an entity possesses a specific flavor or sensory profile, typically describing its characteristic tastes and aromas.
-
C.
wineStyleComparedTo
Indicates a comparison between wines in terms of their style or stylistic characteristics.
-
D.
traditionalGrapeVariety
Indicates that a grape variety is traditionally or historically used in a specific region, wine style, or cultural winemaking practice.
-
E.
wineAcidityType
Indicates the type or category of acidity associated with a given wine.
- 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.