Triple
T11427718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miller Genuine Draft |
E270795
|
entity |
| Predicate | calorieLevel |
P99243
|
FINISHED |
| Object | regular beer |
—
|
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: regular beer | Statement: [Miller Genuine Draft, calorieLevel, regular beer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: calorieLevel Context triple: [Miller Genuine Draft, calorieLevel, regular beer]
-
A.
calorieProfile
Indicates the relationship between an entity and the detailed breakdown of its caloric characteristics, such as total calories and their distribution.
-
B.
hasCalories
Indicates that an entity contains a specified amount of caloric energy.
-
C.
typicalSweetnessLevel
Indicates the usual or characteristic degree of sweetness associated with something.
-
D.
nutritionType
Indicates the specific category or kind of nutritional characteristic or value associated with an entity.
-
E.
nutritionStandard
Indicates that something complies with, or is evaluated against, a defined set of nutritional guidelines or requirements.
- F. None of above. chosen
Provenance (4 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_69d6aadeef688190874bcecd88b3dd9b |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d806c000b88190bfaa646b2dc424b7 |
completed | April 9, 2026, 8:06 p.m. |
| PD | Predicate disambiguation | batch_69d7e71436f88190ac7e45a04ea5c987 |
completed | April 9, 2026, 5:51 p.m. |
| PDg | Predicate description generation | batch_69d80010712c819089ea2e31e664abe1 |
completed | April 9, 2026, 7:37 p.m. |
Created at: April 8, 2026, 9:35 p.m.