Triple
T9044701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | McCune–Reischauer |
E216724
|
entity |
| Predicate | usesBreveOverVowels |
P2270
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [McCune–Reischauer, usesBreveOverVowels, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesBreveOverVowels Context triple: [McCune–Reischauer, usesBreveOverVowels, yes]
-
A.
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).
-
B.
hasNasalVowels
Indicates that the subject language or phonological system includes vowels that are produced with nasal airflow (nasalized vowels).
-
C.
hasVowelHarmony
Indicates that the phonological vowels in a word or morpheme conform to a systematic harmony pattern (e.g., all front or all back vowels) according to the language’s vowel harmony rules.
-
D.
usesDiacritics
chosen
Indicates that the referenced text or linguistic element employs diacritical marks as part of its written form.
-
E.
hasAccent
Indicates that an entity speaks with or possesses a particular accent or distinctive pronunciation 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_69ca83d22d488190adbce5e020e9cd1d |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc6b137cec8190bd1b812c10d9542a |
completed | April 1, 2026, 12:47 a.m. |
| PD | Predicate disambiguation | batch_69cc5ee566b081909e3cdaf551dbd0ec |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:09 p.m.