Triple
T7422028
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nickelodeon Universe |
E171271
|
entity |
| Predicate | hasFoodAndBeverage |
P70493
|
FINISHED |
| Object | concession stands |
—
|
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: concession stands | Statement: [Nickelodeon Universe, hasFoodAndBeverage, concession stands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFoodAndBeverage Context triple: [Nickelodeon Universe, hasFoodAndBeverage, concession stands]
-
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.
hasBeverageCategory
Indicates that an entity is associated with or classified under a particular beverage category.
-
D.
alsoEats
Indicates that an entity consumes something in addition to another item or items it already eats.
-
E.
hasDiningFeature
Indicates that something possesses a specific characteristic, amenity, or attribute related to dining.
- 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_69c68a625d048190af70eb8b63bec5a0 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f2ed29ec8190804564185fe20797 |
completed | March 27, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69c6f03648d08190b862d07fef71210c |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:11 p.m.