Triple
T27827699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gutedel wine |
E703002
|
entity |
| Predicate | typicalAlcoholContentComparedToOtherWhites |
P163342
|
FINISHED |
| Object | low to 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: low to moderate | Statement: [Gutedel wine, typicalAlcoholContentComparedToOtherWhites, low to moderate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAlcoholContentComparedToOtherWhites Context triple: [Gutedel wine, typicalAlcoholContentComparedToOtherWhites, low to moderate]
-
A.
alcoholContentRelativeTo
chosen
Indicates that the alcohol content of one entity is compared to or expressed in relation to the alcohol content of another entity.
-
B.
typicalBlendCabernetFrancPercentage
Indicates the percentage of Cabernet Franc that is typically included in a particular wine blend.
-
C.
typicalBlendMerlotPercentage
Indicates the usual proportion of Merlot used in a blend relative to the other grape varieties.
-
D.
isLowerAlcoholVersionOf
Indicates that one beverage or alcoholic product is a version of another that contains a lower alcohol content.
-
E.
wineDenomination
Indicates that a wine is classified under a specific official denomination or appellation.
- 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_69ef840ad1e88190b5bff2d1ddec8700 |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_6a002249ee388190a9501ee7630dc658 |
completed | May 10, 2026, 6:14 a.m. |
| PD | Predicate disambiguation | batch_6a002189273881909b6b687e2d61f5b1 |
completed | May 10, 2026, 6:11 a.m. |
Created at: April 27, 2026, 5:53 p.m.