Triple
T17495969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blanquette de Limoux |
E426059
|
entity |
| Predicate | traditionalServingTemperature |
P35381
|
FINISHED |
| Object | 6–8 °C |
—
|
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: 6–8 °C | Statement: [Blanquette de Limoux, traditionalServingTemperature, 6–8 °C]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: traditionalServingTemperature Context triple: [Blanquette de Limoux, traditionalServingTemperature, 6–8 °C]
-
A.
recommendedServingTemperature
Indicates the temperature at which something (typically food or drink) is advised to be served for optimal use or enjoyment.
-
B.
wineServingTemperature
chosen
Indicates the temperature at which a particular wine is or should be served.
-
C.
traditionallyServed
Indicates that one entity is customarily or conventionally presented, offered, or consumed together with another entity.
-
D.
servedHot
Indicates that something is provided or presented in a heated or warm state, suitable for immediate consumption.
-
E.
servingStyle
Indicates how something (typically food or drink) is presented or offered for consumption or use.
- 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_69d889dccf7481909264a1844a2e9100 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4520e9c8c8190aa955766bc915d26 |
completed | April 19, 2026, 3:54 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f5fbcc8190a6ea9639bf5650da |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:48 a.m.