Triple
T16132601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tannat |
E391436
|
entity |
| Predicate | wineComponent |
P121596
|
FINISHED |
| Object | high proanthocyanidin content |
—
|
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 proanthocyanidin content | Statement: [Tannat, wineComponent, high proanthocyanidin content]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineComponent Context triple: [Tannat, wineComponent, high proanthocyanidin content]
-
A.
wineCategory
Indicates the classification or type of wine that an entity (such as a specific wine) belongs to.
-
B.
wineStructure
Indicates the overall sensory framework of a wine, encompassing how its components like acidity, tannin, body, and alcohol are balanced and interact.
-
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.
wineName
Indicates the specific name or designation assigned to a wine.
-
E.
wineAVA
Indicates that a wine is produced within a specific American Viticultural Area (AVA) designation.
- 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_69d87f1bb0988190b490d273dbf3fd03 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21a02e0048190b4e2c6ff434c2d7a |
completed | April 17, 2026, 11:31 a.m. |
| PD | Predicate disambiguation | batch_69e1828518c48190a8ef3aaa46a1f639 |
completed | April 17, 2026, 12:44 a.m. |
| PDg | Predicate description generation | batch_69e183b9d3f08190953ada68f4272996 |
completed | April 17, 2026, 12:50 a.m. |
Created at: April 10, 2026, 5:01 a.m.