Triple
T36462707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mainbernheim |
E898333
|
entity |
| Predicate | localWineRegion |
P93466
|
FINISHED |
| Object | Franconian wine region |
—
|
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: Franconian wine region | Statement: [Mainbernheim, localWineRegion, Franconian wine region]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: localWineRegion Context triple: [Mainbernheim, localWineRegion, Franconian wine region]
-
A.
wineLawRegionFocus
Indicates that a legal rule or regulation specifically concerns or targets a particular geographic region in the context of wine.
-
B.
nearWineRegion
Indicates that one entity is located close to or in the vicinity of a wine-producing region.
-
C.
wineRegion
Indicates the geographical region or area where a particular wine is produced or originates.
-
D.
wineSubregion
chosen
Indicates that one region is a subregion within a larger, defined wine-producing region.
-
E.
wineLawRegionType
Indicates the type or category of legal designation that governs wine production in a particular region.
- 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_69f76e58ebd88190b75d9b169b59d793 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fb3425666081908916fcbf3b5dd907 |
completed | May 6, 2026, 12:29 p.m. |
| PD | Predicate disambiguation | batch_69fb2f5f3164819099429c2cc3d24e01 |
completed | May 6, 2026, 12:09 p.m. |
Created at: May 3, 2026, 4:10 p.m.