Triple
T6596960
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akhasheni |
E148499
|
entity |
| Predicate | residualSugar |
P26918
|
FINISHED |
| Object | high residual sugar compared to dry wines |
—
|
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 residual sugar compared to dry wines | Statement: [Akhasheni, residualSugar, high residual sugar compared to dry wines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: residualSugar Context triple: [Akhasheni, residualSugar, high residual sugar compared to dry wines]
-
A.
hasSugarContent
chosen
Indicates that one entity possesses or contains a specified amount or level of sugar.
-
B.
typicalSweetnessLevel
Indicates the usual or characteristic degree of sweetness associated with something.
-
C.
sweetenerType
Indicates the specific kind or category of sweetener associated with or used in relation to an entity.
-
D.
isSugarFree
Indicates that something does not contain sugar or has been formulated without added sugar.
-
E.
hasSugarFreeVariant
Indicates that an item has a corresponding version or option that is formulated without sugar.
- 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_69c687e7b8688190811ffee72e096468 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6acfd17388190bd0bb8b2371e7df1 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:56 p.m.