Triple
T8878943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sangiovese |
E211359
|
entity |
| Predicate | wineTypicalTanninLevel |
P2069
|
FINISHED |
| Object | medium to high |
—
|
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: medium to high | Statement: [Sangiovese, wineTypicalTanninLevel, medium to high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineTypicalTanninLevel Context triple: [Sangiovese, wineTypicalTanninLevel, medium to high]
-
A.
tanninLevel
chosen
Indicates the degree or intensity of tannins present in or associated with something, typically a beverage like wine or tea.
-
B.
wineAcidityType
Indicates the type or category of acidity associated with a given wine.
-
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.
wineColor
Indicates the color attribute or hue associated with a given wine.
-
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_69ca838e78748190934d82db3104f855 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc614ad1908190a77808fcf7f3e531 |
completed | April 1, 2026, 12:05 a.m. |
| PD | Predicate disambiguation | batch_69cc5c2956788190a311c647b4da17a6 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:52 p.m.