Triple
T12970473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yunlong District |
E321380
|
entity |
| Predicate | hasChineseName |
P4878
|
FINISHED |
| Object |
云龙区
云龙区是江苏省徐州市的一个市辖区，以其城市商业、文化教育和交通枢纽功能而闻名。
|
E1013238
|
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: 云龙区 | Statement: [Yunlong District, hasChineseName, 云龙区]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 云龙区 Context triple: [Yunlong District, hasChineseName, 云龙区]
-
A.
曲靖市
曲靖市 is a prefecture-level city in eastern Yunnan Province, China, known as an important industrial and transportation hub in the region.
-
B.
云龙山
云龙山是一座位于中国的山岳景区，以秀美的自然风光和人文景观而闻名。
-
C.
Cangshan District
Cangshan District is an urban district of Fuzhou in Fujian Province, China, known for its mix of historic architecture, educational institutions, and rapidly developing residential and commercial areas.
-
D.
猇亭区
猇亭区 is an urban district of Yichang City in Hubei Province, China, known for its location along the Yangtze River and its role in regional industry and transportation.
-
E.
建邺区
建邺区 is an urban district of Nanjing in Jiangsu Province, China, known for its modern cityscape, government institutions, and location along the Yangtze River.
- 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: 云龙区 Triple: [Yunlong District, hasChineseName, 云龙区]
Generated description
云龙区是江苏省徐州市的一个市辖区，以其城市商业、文化教育和交通枢纽功能而闻名。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 云龙区 Target entity description: 云龙区是江苏省徐州市的一个市辖区，以其城市商业、文化教育和交通枢纽功能而闻名。
-
A.
曲靖市
曲靖市 is a prefecture-level city in eastern Yunnan Province, China, known as an important industrial and transportation hub in the region.
-
B.
云龙山
云龙山是一座位于中国的山岳景区，以秀美的自然风光和人文景观而闻名。
-
C.
Cangshan District
Cangshan District is an urban district of Fuzhou in Fujian Province, China, known for its mix of historic architecture, educational institutions, and rapidly developing residential and commercial areas.
-
D.
猇亭区
猇亭区 is an urban district of Yichang City in Hubei Province, China, known for its location along the Yangtze River and its role in regional industry and transportation.
-
E.
建邺区
建邺区 is an urban district of Nanjing in Jiangsu Province, China, known for its modern cityscape, government institutions, and location along the Yangtze River.
- 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_69d80763bd6c819094437da5b20b01d2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e418d548190be1c73db76cb3aa8 |
completed | April 10, 2026, 10:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6b8e6b31c8190b09276003f284f25 |
completed | May 3, 2026, 2:54 a.m. |
| NEDg | Description generation | batch_69f6b9db8164819086a3a27692d681d5 |
completed | May 3, 2026, 2:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6bb337b708190a874cec01d588236 |
completed | May 3, 2026, 3:04 a.m. |
Created at: April 9, 2026, 8:35 p.m.