Triple
T33604303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norwegian Outlet Vestby |
E860805
|
entity |
| Predicate | hasFoodAndBeverageOptions |
P70493
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Norwegian Outlet Vestby, hasFoodAndBeverageOptions, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFoodAndBeverageOptions Context triple: [Norwegian Outlet Vestby, hasFoodAndBeverageOptions, yes]
-
A.
offersFoodAndBeverage
chosen
Indicates that an entity provides both food and drink to another entity or for general consumption.
-
B.
hasFoodOption
Indicates that an entity offers, provides, or includes a particular type of food or dining option.
-
C.
hasDiningOptionType
Indicates that an entity offers or is associated with a specific type or category of dining option (e.g., dine-in, takeout, delivery).
-
D.
hasCateringServices
Indicates that an entity provides or offers catering services, such as preparing and supplying food and beverages for events or clients.
-
E.
foodTypeOffered
Indicates that one entity offers or serves a particular type or category of food.
- 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_69f3497f35908190a2e9bbb9b96c7a3f |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fda94697c4819081291967202248be |
completed | May 8, 2026, 9:13 a.m. |
| PD | Predicate disambiguation | batch_69fda5973fcc8190a57daef31fb70a49 |
completed | May 8, 2026, 8:57 a.m. |
Created at: May 1, 2026, 1:41 a.m.