Triple
T36664161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Popovača |
E905210
|
entity |
| Predicate | hasLocalWineLabel |
P42729
|
FINISHED |
| Object | Moslavina wines |
—
|
NE NERFINISHED |
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: Moslavina wines | Statement: [Popovača, hasLocalWineLabel, Moslavina wines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocalWineLabel Context triple: [Popovača, hasLocalWineLabel, Moslavina wines]
-
A.
hasWineDenomination
chosen
Indicates that a wine is classified under a specific official denomination or appellation.
-
B.
hasWineBrand
Indicates a relationship where an entity is associated with or offers a specific wine brand.
-
C.
hasWinery
Indicates a relationship where a subject owns, operates, or is associated with a particular winery.
-
D.
hasWineInstitution
Indicates that an entity is associated with, managed by, or belongs to a specific wine-related institution (such as a winery, wine school, or wine organization).
-
E.
hasWineCategory
Indicates that one entity is classified under, or associated with, a particular category or type of wine.
- 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_69f76e6e3b908190970251b30f76ad71 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69ff691f5ae481908597ce245188d31c |
completed | May 9, 2026, 5:04 p.m. |
| PD | Predicate disambiguation | batch_69ff67ceeeb081909fd00cad166c4b6a |
completed | May 9, 2026, 4:58 p.m. |
Created at: May 3, 2026, 4:12 p.m.