Triple
T5931956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dos Equis |
E131957
|
entity |
| Predicate | hasAlcoholByVolume |
P2071
|
FINISHED |
| Object | 4.2% ABV (Lager Especial) |
—
|
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: 4.2% ABV (Lager Especial) | Statement: [Dos Equis, hasAlcoholByVolume, 4.2% ABV (Lager Especial)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAlcoholByVolume Context triple: [Dos Equis, hasAlcoholByVolume, 4.2% ABV (Lager Especial)]
-
A.
alcoholLevel
chosen
Indicates the measured concentration or amount of alcohol present in an entity (such as a person, substance, or environment).
-
B.
alcoholType
Indicates the specific kind or category of alcohol associated with an entity (e.g., beer, wine, spirits).
-
C.
madeWithAlcohol
Indicates that something is created, prepared, or produced using alcohol as an ingredient or component.
-
D.
hasBitternessLevel
Indicates that an entity is associated with a specific degree or intensity of bitterness.
-
E.
colorOfLiquor
Indicates the specific color or hue that a given liquor possesses.
- 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_69c0085b75e88190a632f9691f9da48b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03f26f51881908cc253fe5775a1fc |
completed | March 22, 2026, 7:12 p.m. |
| PD | Predicate disambiguation | batch_69c03355caf08190b960563a1aed23f9 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 4 p.m.