Triple
T4797785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pilsner Urquell Brewery |
E106753
|
entity |
| Predicate | typeOfBeerProduced |
P52234
|
FINISHED |
| Object | Pilsner lager |
—
|
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: Pilsner lager | Statement: [Pilsner Urquell Brewery, typeOfBeerProduced, Pilsner lager]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfBeerProduced Context triple: [Pilsner Urquell Brewery, typeOfBeerProduced, Pilsner lager]
-
A.
breweryType
Indicates the specific category or classification of a brewery based on its operational or business characteristics.
-
B.
beerStyle
chosen
Indicates that one entity is the style or type classification of a beer associated with another entity.
-
C.
beerBrewedBy
Indicates that a particular beer is produced or created by a specific brewer, brewery, or brewing entity.
-
D.
alcoholType
Indicates the specific kind or category of alcohol associated with an entity (e.g., beer, wine, spirits).
-
E.
brewingMethod
Indicates the technique or process used to brew or prepare a beverage, typically coffee or tea.
- 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_69bd43f591c881909e5a532388b0f3f3 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6b40c29c8190adab3503f8ba0145 |
completed | March 20, 2026, 3:44 p.m. |
| PD | Predicate disambiguation | batch_69bd622f88188190a51d52ccfad3d2dd |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:22 p.m.