Triple
T4999087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gurgling Suitcase Libations & Spirits |
E112321
|
entity |
| Predicate | hasAlcoholPolicy |
P34158
|
FINISHED |
| Object | ID required for alcoholic beverage purchase |
—
|
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: ID required for alcoholic beverage purchase | Statement: [Gurgling Suitcase Libations & Spirits, hasAlcoholPolicy, ID required for alcoholic beverage purchase]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAlcoholPolicy Context triple: [Gurgling Suitcase Libations & Spirits, hasAlcoholPolicy, ID required for alcoholic beverage purchase]
-
A.
alcoholPolicy
chosen
Indicates the rules or restrictions governing the possession, sale, or consumption of alcohol in a given context.
-
B.
drinkingPermitted
Indicates that consuming alcoholic beverages is allowed in a given context, location, or situation.
-
C.
servesAlcohol
Indicates that an establishment or provider offers and supplies alcoholic beverages to customers or participants.
-
D.
drinksAlcohol
Indicates that an entity consumes alcoholic beverages.
-
E.
hasNotablePolicy
Indicates that an entity possesses a policy that is distinguished, significant, or otherwise noteworthy in its context.
- 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_69bd4432b32c81909f3b3c6bd10f0653 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7472a1dc8190942f568a81fdd961 |
completed | March 20, 2026, 4:23 p.m. |
| PD | Predicate disambiguation | batch_69bd714aee2481908fb0dd5fa2daf3a1 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:34 p.m.