Triple
T10109912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | hsn |
E218212
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object |
Liling Xiang
Liling Xiang is a regional Chinese dialect spoken in and around Liling in Hunan Province.
|
E842119
|
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: Liling Xiang | Statement: [hsn, hasDialect, Liling Xiang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Liling Xiang Context triple: [hsn, hasDialect, Liling 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.
Pingxiang
Pingxiang is a prefecture-level industrial city in western Jiangxi Province, China, known historically for its coal mining and ceramics production.
-
C.
Huaxiang
Huaxiang is a subdistrict-level area within Beijing’s Fengtai District, known primarily as a residential and urban community zone.
-
D.
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.
-
E.
Zongzhou
Zongzhou was an important ancient Chinese city that served as a central political and ceremonial hub during the Zhou dynasty.
- 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: Liling Xiang Triple: [hsn, hasDialect, Liling Xiang]
Generated description
Liling Xiang is a regional Chinese dialect spoken in and around Liling in Hunan Province.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Liling Xiang Target entity description: Liling Xiang is a regional Chinese dialect spoken in and around Liling in Hunan Province.
-
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.
Pingxiang
Pingxiang is a prefecture-level industrial city in western Jiangxi Province, China, known historically for its coal mining and ceramics production.
-
C.
Huaxiang
Huaxiang is a subdistrict-level area within Beijing’s Fengtai District, known primarily as a residential and urban community zone.
-
D.
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.
-
E.
Zongzhou
Zongzhou was an important ancient Chinese city that served as a central political and ceremonial hub during the Zhou dynasty.
- 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.