Triple
T37778346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serra Gaúcha wine region |
E941750
|
entity |
| Predicate | hasGI |
P189031
|
FINISHED |
| Object | Farroupilha Geographical Indication |
—
|
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: Farroupilha Geographical Indication | Statement: [Serra Gaúcha wine region, hasGI, Farroupilha Geographical Indication]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGI Context triple: [Serra Gaúcha wine region, hasGI, Farroupilha Geographical Indication]
-
A.
hasGood
Indicates that one entity possesses or exhibits a positive, beneficial, or desirable quality, condition, or attribute in relation to another.
-
B.
hasLie
Indicates that an entity is associated with or responsible for a specific lie or false statement.
-
C.
hasGens
Indicates that an entity possesses or is associated with one or more generators (gens), typically as components, resources, or defining elements.
-
D.
has
Indicates that one entity possesses, owns, contains, or includes another entity as part of its state or composition.
-
E.
hasGirth
Indicates that one entity possesses or is characterized by a specific measurement of thickness or circumference.
- 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_69f76ee4431881908f87e8892a9f39f3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbaf48ba148190a8bdfd6846ad0540 |
completed | May 6, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69fbadf632ec8190b14991c971258307 |
completed | May 6, 2026, 9:09 p.m. |
| PDg | Predicate description generation | batch_69fbaeef9c488190babb546b962b4fb6 |
completed | May 6, 2026, 9:13 p.m. |
Created at: May 3, 2026, 4:19 p.m.