Triple
T14852795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ichiba-dori Street |
E349271
|
entity |
| Predicate | typicalCuisineAvailable |
P59511
|
FINISHED |
| Object | Chinese cuisine |
—
|
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: Chinese cuisine | Statement: [Ichiba-dori Street, typicalCuisineAvailable, Chinese cuisine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCuisineAvailable Context triple: [Ichiba-dori Street, typicalCuisineAvailable, Chinese cuisine]
-
A.
haveCuisine
chosen
Indicates that an entity (such as a restaurant or place) offers, serves, or is associated with a particular type or style of cuisine.
-
B.
offersCuisine
Indicates that one entity provides or serves a particular type or style of cuisine to others.
-
C.
isTypicallyServedIn
Indicates that something (such as a food or drink) is most commonly or customarily presented or contained within a particular type of vessel or container.
-
D.
isTypicallyServedFor
Indicates that one item is most commonly or customarily served as a meal or course for the other (e.g., a dish typically served for breakfast, lunch, or dinner).
-
E.
cuisineType
Indicates the type or style of food associated with an entity, such as a restaurant or dish.
- 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_69d822ed7e1881909b90fca143ad7e34 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded441e70881909bbf62b66d932aff |
completed | April 14, 2026, 11:56 p.m. |
| PD | Predicate disambiguation | batch_69de8c1798c08190b433e9ad21e41a42 |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:54 a.m.