Triple
T12347180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crémant de Loire |
E294384
|
entity |
| Predicate | servingTemperatureRecommended |
P66977
|
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: [Crémant de Loire, servingTemperatureRecommended, 6–8 °C]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servingTemperatureRecommended Context triple: [Crémant de Loire, servingTemperatureRecommended, 6–8 °C]
-
A.
recommendedServingTemperature
chosen
Indicates the temperature at which something (typically food or drink) is advised to be served for optimal use or enjoyment.
-
B.
wineServingTemperature
Indicates the temperature at which a particular wine is or should be served.
-
C.
servedHot
Indicates that something is provided or presented in a heated or warm state, suitable for immediate consumption.
-
D.
servedWithIce
Indicates that one item is provided or presented accompanied by ice.
-
E.
servedWithIceType
Indicates that a beverage or drink item is accompanied by and served with a specified type of ice.
- 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_69d6ab6ccbec8190b09e2d357aa80064 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f7ba17481908b03af7316b28d9b |
completed | April 10, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69d93ecb5efc819086a3530282278bb1 |
completed | April 10, 2026, 6:17 p.m. |
Created at: April 8, 2026, 9:53 p.m.