Triple
T29765594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schneider Bräuhaus München |
E753895
|
entity |
| Predicate | hasTypeOfBeer |
P52234
|
FINISHED |
| Object | Hefeweizen |
—
|
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: Hefeweizen | Statement: [Schneider Bräuhaus München, hasTypeOfBeer, Hefeweizen]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfBeer Context triple: [Schneider Bräuhaus München, hasTypeOfBeer, Hefeweizen]
-
A.
hasNotableBeer
Indicates that an entity is associated with a beer that is recognized as distinctive, famous, or otherwise noteworthy.
-
B.
hasFlagshipBeerCharacteristics
Indicates that one beer possesses the defining qualities or signature traits typically associated with a brewery’s primary or flagship beer.
-
C.
isMixedBeer
Indicates that a beer is composed by mixing two or more different beers together.
-
D.
breweryType
Indicates the specific category or classification of a brewery based on its operational or business characteristics.
-
E.
beerStyle
chosen
Indicates that one entity is the style or type classification of a beer associated with another entity.
- 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_69f0ef827ff88190ade56e0b0846b713 |
completed | April 28, 2026, 5:33 p.m. |
| NER | Named-entity recognition | batch_69fd9ff026a48190bfec33deeb3b2c43 |
completed | May 8, 2026, 8:33 a.m. |
| PD | Predicate disambiguation | batch_69fd97d805bc8190ba12f429d3ad04c7 |
completed | May 8, 2026, 7:59 a.m. |
Created at: April 28, 2026, 8:37 p.m.