Triple
T22549848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xiaoerjing |
E557529
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object | Xiao’erjing |
—
|
NE NERFINISHED |
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: Xiao’erjing | Statement: [Xiaoerjing, hasAlternativeName, Xiao’erjing]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xiao’erjing Context triple: [Xiaoerjing, hasAlternativeName, Xiao’erjing]
-
A.
Xiaoerjing
chosen
Xiaoerjing is an Arabic-based writing system historically used to transcribe Sinitic languages, especially by Muslim communities in China such as the Hui.
-
B.
Xiaoyanzi
Xiaoyanzi is a lively, mischievous young woman and one of the main protagonists in the popular Chinese television drama "My Fair Princess" (Huan Zhu Ge Ge).
-
C.
Xiàogǎn
Xiàogǎn is a prefecture-level city in central China’s Hubei province, known for its historical significance and proximity to Wuhan.
-
D.
Xiaozhuan
Xiaozhuan is an ancient standardized form of Chinese calligraphic writing that evolved during the Qin dynasty and served as a key step between earlier scripts and later regular script.
-
E.
Baijiao
Baijiao is a town-level settlement located within Doumen District of Zhuhai in Guangdong Province, China.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e59db848190b4272ecd2b690ffd |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15f74512c8190b5369e19a4bc6325 |
completed | April 29, 2026, 1:31 a.m. |
Created at: April 16, 2026, 8:52 p.m.