Triple
T32023635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grande Champagne (Cognac cru) |
E817759
|
entity |
| Predicate | eauxDeVieStyle |
P173497
|
FINISHED |
| Object | light and delicate |
—
|
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: light and delicate | Statement: [Grande Champagne (Cognac cru), eauxDeVieStyle, light and delicate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eauxDeVieStyle Context triple: [Grande Champagne (Cognac cru), eauxDeVieStyle, light and delicate]
-
A.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
B.
wineCategory
Indicates the classification or type of wine that an entity (such as a specific wine) belongs to.
-
C.
wineDenomination
Indicates that a wine is classified under a specific official denomination or appellation.
-
D.
wineSpeciality
Indicates that an entity (such as a person, place, or establishment) is particularly known for, focused on, or distinguished by a specific type or aspect of wine.
-
E.
wineStructure
Indicates the overall sensory framework of a wine, encompassing how its components like acidity, tannin, body, and alcohol are balanced and interact.
- 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_69f348fb04e4819081f4eab040ed7959 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b56ed31481908c3e5d749e46bad9 |
completed | May 3, 2026, 2:39 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a7bdb481908d16a32f49e38c2c |
completed | May 3, 2026, 2:32 a.m. |
| PDg | Predicate description generation | batch_69f6b49339048190b617a6749f648825 |
completed | May 3, 2026, 2:36 a.m. |
Created at: May 1, 2026, 12:17 a.m.