Triple
T12420226
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kŭmchŏng-gu |
E296748
|
entity |
| Predicate | usesBreveDiacritics |
P2270
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Kŭmchŏng-gu, usesBreveDiacritics, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesBreveDiacritics Context triple: [Kŭmchŏng-gu, usesBreveDiacritics, true]
-
A.
usesDiacritics
chosen
Indicates that the referenced text or linguistic element employs diacritical marks as part of its written form.
-
B.
usesDiacriticsFrom
Indicates that one entity employs or incorporates the diacritical marks that originate from or are characteristic of another entity.
-
C.
hasAccent
Indicates that an entity speaks with or possesses a particular accent or distinctive pronunciation style.
-
D.
usesToneMarks
Indicates that one entity applies or includes diacritical tone marks in the representation or transcription of another entity (such as text, language, or symbols).
-
E.
usesColloquialCharacters
Indicates that an expression, name, or text is written using informal, non-standard, or colloquial characters rather than formal or standard script.
- 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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94e1888b48190bd750f839a26e99e |
completed | April 10, 2026, 7:23 p.m. |
| PD | Predicate disambiguation | batch_69d94d354b488190adc83fb4f2770dd5 |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:55 p.m.