Triple
T24965549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buk-gu, Daegu |
E624730
|
entity |
| Predicate | hasRegionalDialects |
P1762
|
FINISHED |
| Object | Gyeongsang dialect |
—
|
NE NERFINISHED |
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: Gyeongsang dialect | Statement: [Buk-gu, Daegu, hasRegionalDialects, Gyeongsang dialect]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRegionalDialects Context triple: [Buk-gu, Daegu, hasRegionalDialects, Gyeongsang dialect]
-
A.
regionalDialect
chosen
Indicates that one entity uses or is associated with a dialect specific to a particular geographic region in relation to another entity.
-
B.
hasNumberOfDialects
Indicates the relationship between a language (or linguistic entity) and the count of distinct dialects it possesses.
-
C.
hasDialectContinuumWith
Indicates that two languages or dialects are part of a continuous chain of mutually intelligible varieties, without a clear boundary separating them.
-
D.
hasRegionalVariationsIn
Indicates that something exhibits different forms, versions, or characteristics depending on the geographic region.
-
E.
hasDialectalCounterpart
Indicates that one linguistic form has a corresponding equivalent or variant in another dialect.
- 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_69e2ff24512481908e9a72315b8d0354 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f69383222c81909d8baa04129d5c81 |
completed | May 3, 2026, 12:14 a.m. |
| PD | Predicate disambiguation | batch_69f690eb1e948190aab41a89969519a5 |
completed | May 3, 2026, 12:03 a.m. |
Created at: April 18, 2026, 6 a.m.