Triple
T10109911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | hsn |
E218212
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object | Shaoshan Xiang |
E42100
|
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: Shaoshan Xiang | Statement: [hsn, hasDialect, Shaoshan Xiang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shaoshan Xiang Context triple: [hsn, hasDialect, Shaoshan Xiang]
-
A.
Shaoshan
chosen
Shaoshan is a town in Hunan Province, China, best known as the birthplace of Mao Zedong and a significant site of modern Chinese revolutionary history.
-
B.
Xinhua Xiang
Xinhua Xiang is a regional variety of the Xiang group of Chinese dialects spoken in parts of Hunan Province.
-
C.
Huaxiang
Huaxiang is a subdistrict-level area within Beijing’s Fengtai District, known primarily as a residential and urban community zone.
-
D.
Yongqi
Yongqi was a Qing dynasty imperial prince, noted as one of the most talented sons of the Qianlong Emperor before his early death.
-
E.
Xiang
Xiang is the standard abbreviation and common short name used to refer to China’s Hunan Province.
- 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_69ca83da93fc8190b54e44bc2b34857c |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cdd0cdb3c88190a74f75bf865664f3 |
completed | April 2, 2026, 2:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d354e57ea88190922e7eee07fd86f2 |
completed | April 6, 2026, 6:38 a.m. |
Created at: March 30, 2026, 9:03 p.m.