Triple
T9971002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hakata ramen |
E196202
|
entity |
| Predicate | regionalStyleOf |
P11942
|
FINISHED |
| Object | tonkotsu ramen |
—
|
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: tonkotsu ramen | Statement: [Hakata ramen, regionalStyleOf, tonkotsu ramen]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionalStyleOf Context triple: [Hakata ramen, regionalStyleOf, tonkotsu ramen]
-
A.
usesRegionalStyle
Indicates that one entity employs or applies a style, method, or convention characteristic of a particular geographic region in relation to another entity or context.
-
B.
regionalOrientation
Indicates how something is directed, aligned, or focused toward a particular geographic region or area.
-
C.
notableStyleRegion
Indicates that a particular style, manner, or artistic approach is especially characteristic of or prominent within a specific geographic region.
-
D.
regionalDialect
Indicates that one entity uses or is associated with a dialect specific to a particular geographic region in relation to another entity.
-
E.
regionalVariantOf
chosen
Indicates that one entity is a version or form of another that is specific to a particular geographic region or locale.
- 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_69ca82eea2b88190a0e511d21a31f386 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb7b96b1c8190b9d3c1171346615a |
completed | April 2, 2026, 12:26 a.m. |
| PD | Predicate disambiguation | batch_69cd1d9daa808190b413a1b9a1e929e2 |
completed | April 1, 2026, 1:29 p.m. |
Created at: March 30, 2026, 8:48 p.m.