Triple
T25336501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chambourcin |
E635291
|
entity |
| Predicate | hasWineFaultRisk |
P158209
|
FINISHED |
| Object | can show hybrid foxy character if mishandled |
—
|
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: can show hybrid foxy character if mishandled | Statement: [Chambourcin, hasWineFaultRisk, can show hybrid foxy character if mishandled]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWineFaultRisk Context triple: [Chambourcin, hasWineFaultRisk, can show hybrid foxy character if mishandled]
-
A.
hasWineCategory
Indicates that one entity is classified under, or associated with, a particular category or type of wine.
-
B.
usesWineType
Indicates that one entity makes use of, incorporates, or is associated with a particular type or category of wine.
-
C.
hasWinery
Indicates a relationship where a subject owns, operates, or is associated with a particular winery.
-
D.
hasSparklingWine
Indicates that an entity possesses, includes, or is associated with sparkling wine.
-
E.
hasWineRange
Indicates that an entity offers, includes, or is associated with a particular selection or range of wines.
- 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_69e75a99bd6481909476115b35b9a8e4 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f497ca0f18819090168e3221c2aa32 |
completed | May 1, 2026, 12:08 p.m. |
| PD | Predicate disambiguation | batch_69f45d0dbc8c8190beecce679fce90a4 |
completed | May 1, 2026, 7:58 a.m. |
| PDg | Predicate description generation | batch_69f464ae42e88190b3549fdf4e0b425e |
completed | May 1, 2026, 8:30 a.m. |
Created at: April 21, 2026, 1:32 p.m.