Triple
T34394882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suites (Singapore Airlines) |
E882801
|
entity |
| Predicate | beverageService |
P131968
|
FINISHED |
| Object | premium champagne and wines |
—
|
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: premium champagne and wines | Statement: [Suites (Singapore Airlines), beverageService, premium champagne and wines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: beverageService Context triple: [Suites (Singapore Airlines), beverageService, premium champagne and wines]
-
A.
beverageBase
Indicates that one entity serves as the primary liquid or foundational ingredient used to make a beverage involving the other entity.
-
B.
servedDrink
chosen
Indicates that one entity provided or presented a drink to another entity.
-
C.
beverageSubcategory
Indicates a more specific classification within a broader beverage category, defining the subtype or subcategory of a drink.
-
D.
hasBeverageCategory
Indicates that an entity is associated with or classified under a particular beverage category.
-
E.
hasBeverageReference
Indicates that one entity makes reference to, mentions, or is associated with a beverage in some way.
- 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_69f349c1304081909331872829e38106 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71c35327c8190884f1bfe12bd2cd7 |
completed | May 3, 2026, 9:58 a.m. |
| PD | Predicate disambiguation | batch_69f71822d0e88190ac9731c7ae5a4def |
completed | May 3, 2026, 9:40 a.m. |
Created at: May 1, 2026, 1:59 a.m.