Triple
T27971414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taiping Xingguo |
E706365
|
entity |
| Predicate | ChineseCharacters |
P6282
|
FINISHED |
| Object | 太平興國 |
—
|
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: 太平興國 | Statement: [Taiping Xingguo, ChineseCharacters, 太平興國]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ChineseCharacters Context triple: [Taiping Xingguo, ChineseCharacters, 太平興國]
-
A.
usedChineseCharacters
Indicates that one entity employed or wrote using Chinese characters in relation to another entity or context.
-
B.
ChineseNameTraditional
chosen
Indicates that an entity’s name is given in traditional Chinese characters.
-
C.
characterUnicodeSimplified
Indicates that one entity is the simplified-Unicode character form corresponding to another character entity.
-
D.
ChinesePinyin
Indicates that one entity is the Chinese pinyin (romanized phonetic transcription) representation of another entity.
-
E.
hasTraditionalCharacter
Indicates that an entity is associated with or represented by a traditional (non-simplified or historically established) written character form.
- 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_69ef96b7f330819090f315318ba6977e |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69f64dbbaefc8190952b8320bf4397d8 |
completed | May 2, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f64cacd2c08190aed8a1761d0da679 |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 27, 2026, 7:38 p.m.