Triple
T3955067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huizhou (historical region) |
E84955
|
entity |
| Predicate | hasCulinaryStyle |
P5786
|
FINISHED |
| Object | Huizhou 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: Huizhou cuisine | Statement: [Huizhou (historical region), hasCulinaryStyle, Huizhou cuisine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCulinaryStyle Context triple: [Huizhou (historical region), hasCulinaryStyle, Huizhou cuisine]
-
A.
hasCuisineItem
Indicates that a particular cuisine includes, features, or is associated with a specific food item.
-
B.
cuisineType
chosen
Indicates the type or style of food associated with an entity, such as a restaurant or dish.
-
C.
cuisineFeature
Indicates a characteristic, quality, or notable aspect that describes or distinguishes a particular cuisine.
-
D.
hasSpecialtyFood
Indicates that an entity offers, serves, or is associated with a particular type of specialty food.
-
E.
diningStyle
Indicates the manner or format in which dining is conducted, such as casual, formal, buffet, or family-style.
- 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_69aed934fbfc8190847068e4546de963 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaa5afdc8190b709af2473d75d02 |
completed | March 9, 2026, 4:51 p.m. |
| PD | Predicate disambiguation | batch_69aef8ed04e4819096bced8971cd888d |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:30 p.m.