Triple
T10109914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | hsn |
E218212
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object | Yiyang Xiang |
E172641
|
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: Yiyang Xiang | Statement: [hsn, hasDialect, Yiyang Xiang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yiyang Xiang Context triple: [hsn, hasDialect, Yiyang Xiang]
-
A.
Ningxiang
Ningxiang is a county-level city in Hunan Province, China, administered by the prefecture-level city of Changsha and known for its rapidly developing economy and rich cultural heritage.
-
B.
Yiyang
chosen
Yiyang is a prefecture-level city in south-central China known for its location along the Zi River and its role as an important regional center in Hunan Province.
-
C.
Xiangxiang City
Xiangxiang City is a county-level city in Hunan Province, China, administered by the prefecture-level city of Xiangtan and known for its historical and cultural heritage.
-
D.
Shaoyang
Shaoyang is a prefecture-level city in south-central China known for its long history, cultural heritage, and location in the southwestern part of Hunan Province.
-
E.
Huaihua
Huaihua is a prefecture-level city in southwestern Hunan Province, China, known as a regional transportation hub and home to several ethnic minority communities.
- 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_69d71c559910819092b0eae9c05aa7dc |
completed | April 9, 2026, 3:26 a.m. |
Created at: March 30, 2026, 9:03 p.m.