Triple
T30920192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leobersdorf |
E787696
|
entity |
| Predicate | hasWineCulture |
P59581
|
FINISHED |
| Object | Heuriger taverns |
—
|
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: Heuriger taverns | Statement: [Leobersdorf, hasWineCulture, Heuriger taverns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWineCulture Context triple: [Leobersdorf, hasWineCulture, Heuriger taverns]
-
A.
hasWineMakingTradition
chosen
Indicates that a place or group has an established, culturally recognized history and practice of producing wine.
-
B.
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).
-
C.
nationalWineTradition
Indicates that a country or region has an established cultural and historical practice of producing, consuming, and valuing wine.
-
D.
sharesWineAndBeerCulture
Indicates that two entities have a common cultural tradition or practice involving both wine and beer.
-
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_69f224bfaca88190b9d0dfcc86297fe9 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6d6a6b04c8190bee4cf9c00665ef7 |
completed | May 3, 2026, 5:01 a.m. |
| PD | Predicate disambiguation | batch_69f6d26ceb08819091c71c001e954936 |
completed | May 3, 2026, 4:43 a.m. |
Created at: April 29, 2026, 8:51 p.m.