Triple
T15934384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Splitsville Dining Room |
E386402
|
entity |
| Predicate | servesBeverageType |
P30390
|
FINISHED |
| Object | soft drinks |
—
|
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: soft drinks | Statement: [Splitsville Dining Room, servesBeverageType, soft drinks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesBeverageType Context triple: [Splitsville Dining Room, servesBeverageType, soft drinks]
-
A.
hasBeverageCategory
Indicates that an entity is associated with or classified under a particular beverage category.
-
B.
beverageSubcategory
Indicates a more specific classification within a broader beverage category, defining the subtype or subcategory of a drink.
-
C.
hasCaffeinatedOption
Indicates that something offers or includes at least one option that contains caffeine.
-
D.
hasBeverageReference
Indicates that one entity makes reference to, mentions, or is associated with a beverage in some way.
-
E.
featuresBeverage
chosen
Indicates that one entity includes, offers, or presents a particular beverage as part of its contents, services, or characteristics.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e17d4d08f481909f38b75e3f42d9ab |
completed | April 17, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e142d37cd88190ab50760f1783e20c |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:53 a.m.