Triple
T28020411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Graves wine region |
E707669
|
entity |
| Predicate | typicalAlcoholLevelWhite |
P124347
|
FINISHED |
| Object | moderate |
—
|
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: moderate | Statement: [Graves wine region, typicalAlcoholLevelWhite, moderate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAlcoholLevelWhite Context triple: [Graves wine region, typicalAlcoholLevelWhite, moderate]
-
A.
minimumAlcoholWhite
Indicates the minimum required alcohol content specified for white wine.
-
B.
alcoholType
Indicates the specific kind or category of alcohol associated with an entity (e.g., beer, wine, spirits).
-
C.
alcoholStrengthCategory
chosen
Indicates the classification of an alcoholic beverage based on the strength or concentration of its alcohol content.
-
D.
alcoholRange
Indicates the range or interval of alcohol content associated with an entity (e.g., minimum and maximum alcohol level).
-
E.
alcoholLevel
Indicates the measured concentration or amount of alcohol present in an entity (such as a person, substance, or environment).
- 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_69ef96baf3a881909a2b63844185dddd |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_6a0059f4ffe481908dc500cee9051148 |
completed | May 10, 2026, 10:12 a.m. |
| PD | Predicate disambiguation | batch_6a00593a3c1881909b2ff1a29eb474b3 |
completed | May 10, 2026, 10:08 a.m. |
Created at: April 27, 2026, 8:09 p.m.