Triple
T2328846
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Entre-Deux-Mers |
E48353
|
entity |
| Predicate | wineServingSuggestion |
P38944
|
FINISHED |
| Object | served young |
—
|
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: served young | Statement: [Entre-Deux-Mers, wineServingSuggestion, served young]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineServingSuggestion Context triple: [Entre-Deux-Mers, wineServingSuggestion, served young]
-
A.
wineServingTemperature
Indicates the temperature at which a particular wine is or should be served.
-
B.
wineProgram
Indicates a relationship where an entity is part of, offered through, or associated with a specific wine-related program (such as a membership, curriculum, or organized initiative focused on wine).
-
C.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
D.
wineStructure
Indicates the overall sensory framework of a wine, encompassing how its components like acidity, tannin, body, and alcohol are balanced and interact.
-
E.
wineAgeingPreference
Indicates a preference for how long wine should be aged before consumption or use.
- F. None of above. chosen
Provenance (4 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_69a88aa308a88190b0b86c011fda7fce |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abcc30c5e881908c5d526d7e7491d0 |
completed | March 7, 2026, 6:56 a.m. |
| PD | Predicate disambiguation | batch_69abc5926d048190a535e3f23d41de2a |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abcc2fa25c8190858c1c541b914f4c |
completed | March 7, 2026, 6:56 a.m. |
Created at: March 4, 2026, 7:50 p.m.