Triple
T22676529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maximus IPA |
E560359
|
entity |
| Predicate | alcoholContentRelative |
P124347
|
FINISHED |
| Object | high ABV |
—
|
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: high ABV | Statement: [Maximus IPA, alcoholContentRelative, high ABV]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alcoholContentRelative Context triple: [Maximus IPA, alcoholContentRelative, high ABV]
-
A.
alcoholLevel
Indicates the measured concentration or amount of alcohol present in an entity (such as a person, substance, or environment).
-
B.
alcoholStrengthCategory
chosen
Indicates the classification of an alcoholic beverage based on the strength or concentration of its alcohol content.
-
C.
featuresAlcoholReference
Indicates that the subject includes or contains a reference to alcohol or alcoholic beverages.
-
D.
alcoholRange
Indicates the range or interval of alcohol content associated with an entity (e.g., minimum and maximum alcohol level).
-
E.
alcoholType
Indicates the specific kind or category of alcohol associated with an entity (e.g., beer, wine, spirits).
- 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_69e2454bfd00819099115715a22cb057 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1785ca1e08190af1a6cdb51ca4fce |
completed | April 29, 2026, 3:17 a.m. |
| PD | Predicate disambiguation | batch_69ee62a6245881909506ff502da14137 |
completed | April 26, 2026, 7:08 p.m. |
Created at: April 17, 2026, 3:11 p.m.