Triple
T25336447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catawba |
E635290
|
entity |
| Predicate | wineSweetnessTendency |
P62205
|
FINISHED |
| Object | often vinified sweet |
—
|
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: often vinified sweet | Statement: [Catawba, wineSweetnessTendency, often vinified sweet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineSweetnessTendency Context triple: [Catawba, wineSweetnessTendency, often vinified sweet]
-
A.
sparklingWineSweetnessRange
Indicates the range of sweetness levels that a sparkling wine can have or is classified within.
-
B.
typicalSweetnessLevel
chosen
Indicates the usual or characteristic degree of sweetness associated with something.
-
C.
wineAcidityType
Indicates the type or category of acidity associated with a given wine.
-
D.
hasSherrySweetness
Indicates that one entity possesses or exhibits the sweetness characteristic associated with sherry.
-
E.
sweetWineSuitability
Indicates the degree to which something is appropriate or recommended for pairing with or serving as a sweet wine.
- 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_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. |
Created at: April 21, 2026, 1:32 p.m.