Triple
T35548386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | One Mint Julep |
E1027281
|
entity |
| Predicate | involvesAlcoholInPlot |
P134761
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [One Mint Julep, involvesAlcoholInPlot, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvesAlcoholInPlot Context triple: [One Mint Julep, involvesAlcoholInPlot, true]
-
A.
alcohol
Indicates that one entity is an alcoholic beverage or contains alcohol in relation to another entity.
-
B.
madeWithAlcohol
Indicates that something is created, prepared, or produced using alcohol as an ingredient or component.
-
C.
alcoholicCategory
Indicates that one entity is classified as a type or category within the domain of alcoholic beverages in relation to another entity.
-
D.
isAlcoholicInFiction
Indicates that, within a fictional context, a character is portrayed as having alcoholism or a problematic dependence on alcohol.
-
E.
hasAlcoholTheme
chosen
Indicates that the subject involves, features, or is centered around alcohol-related content or themes.
- 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_69f76e008ba08190927acd8e5e0344c8 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fe189fec148190aeef51b417ba15b0 |
completed | May 8, 2026, 5:08 p.m. |
| PD | Predicate disambiguation | batch_69fe17285b0881908de7569d8dbd20bd |
completed | May 8, 2026, 5:02 p.m. |
Created at: May 3, 2026, 4:04 p.m.