Triple
T570975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Howell Mountain AVA |
E13661
|
entity |
| Predicate | wineCharacteristic |
P16142
|
FINISHED |
| Object | high tannin |
—
|
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: high tannin | Statement: [Howell Mountain AVA, wineCharacteristic, high tannin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineCharacteristic Context triple: [Howell Mountain AVA, wineCharacteristic, high tannin]
-
A.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
B.
viticulturalCharacteristic
Indicates a relationship where a specific trait, quality, or property is attributed to viticulture or grape-growing practices.
-
C.
wineColor
Indicates the color attribute or hue associated with a given wine.
-
D.
wineClassificationSystem
Indicates a system or scheme used to categorize and organize wines based on defined criteria such as origin, style, or quality.
-
E.
wineAgeingPotential
Indicates the capacity or suitability of a wine to improve in quality or maintain desirable characteristics over time with proper aging.
- 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_69a4933fa4d88190a7949cc83c08c5c1 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49b483ac08190b3be152a7cf42011 |
completed | March 1, 2026, 8:02 p.m. |
| PD | Predicate disambiguation | batch_69a494c2caac819086ab316fa49d324c |
completed | March 1, 2026, 7:34 p.m. |
| PDg | Predicate description generation | batch_69a498dd579081908e02368a4c5efc8c |
completed | March 1, 2026, 7:51 p.m. |
Created at: March 1, 2026, 7:33 p.m.