Triple
T1334570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyushu dialect |
E28718
|
entity |
| Predicate | usesScript |
P1587
|
FINISHED |
| Object | Hiragana |
E3828
|
NE 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: Hiragana | Statement: [Kyushu dialect, usesScript, Hiragana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hiragana Context triple: [Kyushu dialect, usesScript, Hiragana]
-
A.
Hiragana
chosen
Hiragana is a Japanese phonetic syllabary used primarily for native words, grammatical elements, and beginners’ reading and writing.
-
B.
Kanji
Kanji are logographic characters of Chinese origin used in the Japanese writing system alongside hiragana and katakana.
-
C.
Kawi script
Kawi script is an ancient Brahmic-derived writing system historically used across Java and other parts of Southeast Asia to write Old Javanese and related languages.
-
D.
Hanja
Hanja is the set of traditional Chinese characters historically used to write Korean, especially for proper names, academic terms, and classical texts.
-
E.
Hangul
Hangul is the native alphabetic writing system of the Korean language, renowned for its scientific design and ease of learning.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a498561a508190a3e1bc137c2b866a |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1eb119881909dd5fbf728d9e8ba |
completed | March 1, 2026, 10:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acbf3943f88190af908723f87e50e2 |
completed | March 8, 2026, 12:13 a.m. |
Created at: March 1, 2026, 7:55 p.m.