Triple
T23102424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Humagne Rouge |
E576067
|
entity |
| Predicate | wineTannins |
P2069
|
FINISHED |
| Object | firm |
—
|
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: firm | Statement: [Humagne Rouge, wineTannins, firm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineTannins Context triple: [Humagne Rouge, wineTannins, firm]
-
A.
wineTanninSource
Indicates the source from which the tannins present in a wine are derived (e.g., grape skins, seeds, stems, or oak).
-
B.
tanninLevel
chosen
Indicates the degree or intensity of tannins present in or associated with something, typically a beverage like wine or tea.
-
C.
wineCharacteristic
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
D.
wineVariety
Indicates the specific type or variety of wine associated with an entity.
-
E.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
- 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_69e245c060b48190a9bd61a47a16db17 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18de9fa8c81909fd26ff37173b85b |
completed | April 29, 2026, 4:49 a.m. |
| PD | Predicate disambiguation | batch_69ef89e5ce748190b2c3ac3843484127 |
completed | April 27, 2026, 4:08 p.m. |
Created at: April 17, 2026, 3:58 p.m.