Triple
T13015658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hangzhounese |
E322543
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object |
Hangzhouhua
Hangzhouhua is a regional Chinese dialect spoken in and around the city of Hangzhou in Zhejiang province.
|
E1016655
|
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: Hangzhouhua | Statement: [Hangzhounese, hasAlternativeName, Hangzhouhua]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hangzhouhua Context triple: [Hangzhounese, hasAlternativeName, Hangzhouhua]
-
A.
Dōngguǎn
Dōngguǎn is a major industrial and manufacturing city in Guangdong Province, China, known for its extensive export-oriented factories and role in the Pearl River Delta economic region.
-
B.
Xitang
Xitang is an ancient water town in eastern China renowned for its well-preserved canals, stone bridges, and traditional architecture.
-
C.
Bianliang
Bianliang is the historical name of the Chinese city that served as the capital during the Northern Song dynasty, now known as Kaifeng.
-
D.
Shangyuan
Shangyuan was a Chinese imperial era name used during the reign of Emperor Suzong of the Tang dynasty.
-
E.
Liyang
Liyang is a county-level city in Jiangsu Province, China, known for its scenic attractions such as Tianmu Lake and its administration under the prefecture-level city of Changzhou.
- 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: Hangzhouhua Triple: [Hangzhounese, hasAlternativeName, Hangzhouhua]
Generated description
Hangzhouhua is a regional Chinese dialect spoken in and around the city of Hangzhou in Zhejiang province.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hangzhouhua Target entity description: Hangzhouhua is a regional Chinese dialect spoken in and around the city of Hangzhou in Zhejiang province.
-
A.
Dōngguǎn
Dōngguǎn is a major industrial and manufacturing city in Guangdong Province, China, known for its extensive export-oriented factories and role in the Pearl River Delta economic region.
-
B.
Xitang
Xitang is an ancient water town in eastern China renowned for its well-preserved canals, stone bridges, and traditional architecture.
-
C.
Bianliang
Bianliang is the historical name of the Chinese city that served as the capital during the Northern Song dynasty, now known as Kaifeng.
-
D.
Shangyuan
Shangyuan was a Chinese imperial era name used during the reign of Emperor Suzong of the Tang dynasty.
-
E.
Liyang
Liyang is a county-level city in Jiangsu Province, China, known for its scenic attractions such as Tianmu Lake and its administration under the prefecture-level city of Changzhou.
- 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_69d807657e8c8190bd9435ee2f823845 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97ecd04748190ade2530ee5db35fe |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c1147974819090007c21383d5c86 |
completed | May 3, 2026, 3:29 a.m. |
| NEDg | Description generation | batch_69f6c562d10c8190b76dbf50a0101bae |
completed | May 3, 2026, 3:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6c635fc888190891a79da9d7984a0 |
completed | May 3, 2026, 3:51 a.m. |
Created at: April 9, 2026, 8:50 p.m.