Triple
T7234626
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aconcagua Valley |
E155190
|
entity |
| Predicate | typicalSyrahProfile |
P16142
|
FINISHED |
| Object | spicy, full-bodied wines |
—
|
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: spicy, full-bodied wines | Statement: [Aconcagua Valley, typicalSyrahProfile, spicy, full-bodied wines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSyrahProfile Context triple: [Aconcagua Valley, typicalSyrahProfile, spicy, full-bodied wines]
-
A.
wineCharacteristic
chosen
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
B.
typicalViognierPercentage
Indicates the usual proportion of Viognier used within a given wine, blend, or production context.
-
C.
wineAcidityType
Indicates the type or category of acidity associated with a given wine.
-
D.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
E.
wineStyleComparedTo
Indicates a comparison between wines in terms of their style or stylistic characteristics.
- 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_69c688143bfc81908d4176617735e601 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ea552a688190a00f5d0ad982f787 |
completed | March 27, 2026, 8:36 p.m. |
| PD | Predicate disambiguation | batch_69c6e7644648819096a5e2de5d0dbe97 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 2:55 p.m.