Triple
T28020410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Graves wine region |
E707669
|
entity |
| Predicate | typicalAlcoholLevelRed |
P89196
|
FINISHED |
| Object | moderate to high |
—
|
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 to high | Statement: [Graves wine region, typicalAlcoholLevelRed, moderate to high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAlcoholLevelRed Context triple: [Graves wine region, typicalAlcoholLevelRed, moderate to high]
-
A.
typicalAlcoholRangeRed
chosen
Indicates that the subject red wine typically falls within a specified range of alcohol content.
-
B.
minimumAlcoholRed
Indicates that there is a specified minimum alcohol content requirement associated with red wine or red alcoholic beverages.
-
C.
alcoholLevel
Indicates the measured concentration or amount of alcohol present in an entity (such as a person, substance, or environment).
-
D.
alcoholRange
Indicates the range or interval of alcohol content associated with an entity (e.g., minimum and maximum alcohol level).
-
E.
regulatesAlcoholLevel
Indicates a relationship where one entity controls, adjusts, or maintains the alcohol level of another entity or system.
- 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_6a0058c341ac8190825067dc25158839 |
completed | May 10, 2026, 10:06 a.m. |
| PD | Predicate disambiguation | batch_6a005857249c81908b27587b84d84dbb |
completed | May 10, 2026, 10:05 a.m. |
Created at: April 27, 2026, 8:09 p.m.