Triple
T10109927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | hsn |
E218212
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object |
Anhua Xiang
Anhua Xiang is a regional variety of the Xiang Chinese language spoken primarily in Anhua County, Hunan Province, China.
|
E843761
|
NE FINISHED |
How this triple was built (4 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: Anhua Xiang | Statement: [hsn, hasDialect, Anhua Xiang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anhua Xiang Context triple: [hsn, hasDialect, Anhua Xiang]
-
A.
Liling Xiang
Liling Xiang is a regional Chinese dialect spoken in and around Liling in Hunan Province.
-
B.
Fenghuang Xiang
Fenghuang Xiang is a regional variety of the Xiang Chinese language spoken in and around Fenghuang County in Hunan Province, China.
-
C.
Huaxiang
Huaxiang is a subdistrict-level area within Beijing’s Fengtai District, known primarily as a residential and urban community zone.
-
D.
Shuangfeng Old Xiang
Shuangfeng Old Xiang is a traditional variety of the Old Xiang Chinese language spoken primarily in Shuangfeng County, Hunan Province.
-
E.
Yuxiang
Yuxiang is a Chinese given name notably borne by the early 20th-century warlord and military leader Feng Yuxiang.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Anhua Xiang Triple: [hsn, hasDialect, Anhua Xiang]
Generated description
Anhua Xiang is a regional variety of the Xiang Chinese language spoken primarily in Anhua County, Hunan Province, China.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anhua Xiang Target entity description: Anhua Xiang is a regional variety of the Xiang Chinese language spoken primarily in Anhua County, Hunan Province, China.
-
A.
Liling Xiang
Liling Xiang is a regional Chinese dialect spoken in and around Liling in Hunan Province.
-
B.
Fenghuang Xiang
Fenghuang Xiang is a regional variety of the Xiang Chinese language spoken in and around Fenghuang County in Hunan Province, China.
-
C.
Huaxiang
Huaxiang is a subdistrict-level area within Beijing’s Fengtai District, known primarily as a residential and urban community zone.
-
D.
Shuangfeng Old Xiang
Shuangfeng Old Xiang is a traditional variety of the Old Xiang Chinese language spoken primarily in Shuangfeng County, Hunan Province.
-
E.
Yuxiang
Yuxiang is a Chinese given name notably borne by the early 20th-century warlord and military leader Feng Yuxiang.
- F. None of above. chosen
Provenance (5 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_69d2e59ec83c8190a79fbb0d0de90310 |
completed | April 5, 2026, 10:43 p.m. |
| NEDg | Description generation | batch_69d2e6f0aa988190aa9a866afcc2a1a2 |
completed | April 5, 2026, 10:49 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d2e78384f48190abb7bdd7fcadcd9a |
completed | April 5, 2026, 10:51 p.m. |
Created at: March 30, 2026, 9:03 p.m.