Triple
T7519391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marsannay |
E177727
|
entity |
| Predicate | typicalServingTemperatureWhite |
P35381
|
FINISHED |
| Object | around 10–12 °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: around 10–12 °C | Statement: [Marsannay, typicalServingTemperatureWhite, around 10–12 °C]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalServingTemperatureWhite Context triple: [Marsannay, typicalServingTemperatureWhite, around 10–12 °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.
brewingTemperature
Indicates the specific temperature at which a brewing process is carried out.
-
D.
servingStyle
Indicates how something (typically food or drink) is presented or offered for consumption or use.
-
E.
whiteWineShare
Indicates the proportion or share of white wine within a larger set, such as total wine consumption, production, or sales.
- 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_69c69f2891148190a484f3b8222c6f1b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5f98ae48190946a18d7c2d33bcd |
completed | March 27, 2026, 9:26 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d6bb808190bdd04499fd3bceb6 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:46 p.m.