Triple
T343630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Burgundy |
E6890
|
entity |
| Predicate | wineColor |
P12088
|
FINISHED |
| Object | red wine |
—
|
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: red wine | Statement: [Burgundy, wineColor, red wine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineColor Context triple: [Burgundy, wineColor, red wine]
-
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.
primaryGrapeVariety
Indicates that one entity is the main or predominant grape variety used in producing the other entity (typically a wine or wine-based product).
-
C.
traditionalDrink
Indicates that one entity is a beverage customarily consumed within the culture, heritage, or longstanding practices associated with another entity.
-
D.
wineAgeingPotential
Indicates the capacity or suitability of a wine to improve in quality or maintain desirable characteristics over time with proper aging.
-
E.
wineRegion
Indicates the geographical region or area where a particular wine is produced or originates.
- 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_69a2e7951ba08190960e90823b5078f3 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eb0019088190a9b969c4287dc4fa |
completed | Feb. 28, 2026, 1:17 p.m. |
| PD | Predicate disambiguation | batch_69a2e9530c98819085025efe4e04aa7e |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea0a4c448190a8a179daa9b90645 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.