Triple
T21652344
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vale do São Francisco |
E534368
|
entity |
| Predicate | wineRegionCharacteristic |
P87852
|
FINISHED |
| Object | tropical viticulture |
—
|
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: tropical viticulture | Statement: [Vale do São Francisco, wineRegionCharacteristic, tropical viticulture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineRegionCharacteristic Context triple: [Vale do São Francisco, wineRegionCharacteristic, tropical viticulture]
-
A.
wineCharacteristic
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
B.
wineRegionCategory
Indicates a classification relationship where a wine region is assigned to a specific category or type of wine-producing area.
-
C.
wineClassificationRegion
Indicates that a wine is classified according to the geographic region where it is produced or designated.
-
D.
wineRegionSpecialization
chosen
Indicates a relationship where a wine-producing region is characterized by or particularly known for specializing in a specific type or style of wine.
-
E.
wineRegion
Indicates the geographical region or area where a particular wine is produced or originates.
- 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_69e0c466aec88190ba39c7543dbc8ba2 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef591594a08190bf0ddd0a0c0922ba |
completed | April 27, 2026, 12:39 p.m. |
| PD | Predicate disambiguation | batch_69e696826c3c81909270791e79760937 |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:36 p.m.