Triple
T28075475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 韓 |
E709528
|
entity |
| Predicate | hasZhuyin |
P52966
|
FINISHED |
| Object | ㄏㄢˊ |
—
|
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: ㄏㄢˊ | Statement: [韓, hasZhuyin, ㄏㄢˊ]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasZhuyin Context triple: [韓, hasZhuyin, ㄏㄢˊ]
-
A.
hasSyllabary
Indicates that one entity possesses or is associated with a specific syllabary writing system used to represent its language or notation.
-
B.
hasMandarinReading
Indicates that an entity is associated with a specific reading or pronunciation in Mandarin Chinese.
-
C.
mandarinReadingBopomofo
chosen
Indicates the Bopomofo (Zhuyin) phonetic transcription used to represent the Mandarin pronunciation of a given expression or character.
-
D.
hasTraditionalCharacter
Indicates that an entity is associated with or represented by a traditional (non-simplified or historically established) written character form.
-
E.
ChinesePinyin
Indicates that one entity is the Chinese pinyin (romanized phonetic transcription) representation of another entity.
- 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_69ef9b6f8078819098b741274cd1a2ee |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f6403f29d481909168f09c19dcbb18 |
completed | May 2, 2026, 6:19 p.m. |
| PD | Predicate disambiguation | batch_69f637120c008190b2b5adf46f022e48 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 27, 2026, 8:48 p.m.