Triple
T10109918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | hsn |
E218212
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object |
Xinhua Xiang
Xinhua Xiang is a regional variety of the Xiang group of Chinese dialects spoken in parts of Hunan Province.
|
E842120
|
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: Xinhua Xiang | Statement: [hsn, hasDialect, Xinhua Xiang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xinhua Xiang Context triple: [hsn, hasDialect, Xinhua Xiang]
-
A.
Yuxiang
Yuxiang is a Chinese given name notably borne by the early 20th-century warlord and military leader Feng Yuxiang.
-
B.
Huaxiang
Huaxiang is a subdistrict-level area within Beijing’s Fengtai District, known primarily as a residential and urban community zone.
-
C.
Linxiang
Linxiang is a county-level city administered by Yueyang in Hunan Province, China, known for its location near the Yangtze River and its regional agricultural and industrial activities.
-
D.
Xiaochang
Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
-
E.
Yong–Quan Xiang
Yong–Quan Xiang is a regional variety of the Xiang group of Chinese dialects spoken in parts of Hunan Province.
- 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: Xinhua Xiang Triple: [hsn, hasDialect, Xinhua Xiang]
Generated description
Xinhua Xiang is a regional variety of the Xiang group of Chinese dialects spoken in parts of Hunan Province.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Xinhua Xiang Target entity description: Xinhua Xiang is a regional variety of the Xiang group of Chinese dialects spoken in parts of Hunan Province.
-
A.
Yuxiang
Yuxiang is a Chinese given name notably borne by the early 20th-century warlord and military leader Feng Yuxiang.
-
B.
Huaxiang
Huaxiang is a subdistrict-level area within Beijing’s Fengtai District, known primarily as a residential and urban community zone.
-
C.
Linxiang
Linxiang is a county-level city administered by Yueyang in Hunan Province, China, known for its location near the Yangtze River and its regional agricultural and industrial activities.
-
D.
Xiaochang
Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
-
E.
Yong–Quan Xiang
Yong–Quan Xiang is a regional variety of the Xiang group of Chinese dialects spoken in parts of Hunan Province.
- 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_69d2cc1805d08190bc39aadf1e84a569 |
completed | April 5, 2026, 8:54 p.m. |
| NEDg | Description generation | batch_69d2cd8f0a688190a437b7e2d158c70c |
completed | April 5, 2026, 9:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d2ce422e4c8190b54b94cdfa0c4c98 |
completed | April 5, 2026, 9:04 p.m. |
Created at: March 30, 2026, 9:03 p.m.