Triple
T8257292
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sanrio Puroland |
E193101
|
entity |
| Predicate | hasRestaurantType |
P81236
|
FINISHED |
| Object | character-themed restaurant |
—
|
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: character-themed restaurant | Statement: [Sanrio Puroland, hasRestaurantType, character-themed restaurant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRestaurantType Context triple: [Sanrio Puroland, hasRestaurantType, character-themed restaurant]
-
A.
hasRestaurant
Indicates that one entity possesses, operates, or contains a restaurant associated with it.
-
B.
isDiningDestination
Indicates that a place serves as a destination where people go specifically to eat meals or dine.
-
C.
alsoEats
Indicates that an entity consumes something in addition to another item or items it already eats.
-
D.
restaurantName
Indicates the name assigned to a restaurant as its identifying label.
-
E.
hasRestaurantsAndCafes
Indicates that the subject location contains or provides access to restaurants and cafés.
- F. None of above. chosen
Provenance (4 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_69ca82dfad9c8190b8cd18fb89f50f40 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb78fce9308190ac36512e80b06b52 |
completed | March 31, 2026, 7:34 a.m. |
| PD | Predicate disambiguation | batch_69cb36b6d5548190b665a6cce14c69f7 |
completed | March 31, 2026, 2:51 a.m. |
| PDg | Predicate description generation | batch_69cb44d1caa881909069fa925a91316b |
completed | March 31, 2026, 3:51 a.m. |
Created at: March 30, 2026, 5:49 p.m.