Triple
T10669642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xicheng District, Beijing |
E251452
|
entity |
| Predicate | hasChineseName |
P4878
|
FINISHED |
| Object |
西城区
西城区是中国北京市中心的一个重要城区,以其丰富的历史文化遗产和众多政府机关、金融机构的集中分布而著称。
|
E879515
|
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: 西城区 | Statement: [Xicheng District, Beijing, hasChineseName, 西城区]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 西城区 Context triple: [Xicheng District, Beijing, hasChineseName, 西城区]
-
A.
Daxing District
Daxing District is a rapidly developing suburban district in southern Beijing, China, known for hosting the major Beijing Daxing International Airport and large-scale urban expansion.
-
B.
Haidian Subdistrict
Haidian Subdistrict is the central urban area and seat of local government within Beijing’s Haidian District, known for its dense commercial and residential development.
-
C.
Haidian District
Haidian District is a major urban district in northwest Beijing known for its universities, technology hubs, and historic imperial gardens.
-
D.
北京皇城
北京皇城是明清两代北京城中围绕皇宫设置的内城区域,汇集重要宫殿、坛庙和皇家建筑群的核心防御与礼制空间。
-
E.
Dongcheng District, Beijing
Dongcheng District, Beijing is a central urban district of China's capital city that encompasses many of its most important political, historical, and cultural landmarks, including Tiananmen Square and the Forbidden City.
- 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: 西城区 Triple: [Xicheng District, Beijing, hasChineseName, 西城区]
Generated description
西城区是中国北京市中心的一个重要城区,以其丰富的历史文化遗产和众多政府机关、金融机构的集中分布而著称。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 西城区 Target entity description: 西城区是中国北京市中心的一个重要城区,以其丰富的历史文化遗产和众多政府机关、金融机构的集中分布而著称。
-
A.
Daxing District
Daxing District is a rapidly developing suburban district in southern Beijing, China, known for hosting the major Beijing Daxing International Airport and large-scale urban expansion.
-
B.
Haidian Subdistrict
Haidian Subdistrict is the central urban area and seat of local government within Beijing’s Haidian District, known for its dense commercial and residential development.
-
C.
Haidian District
Haidian District is a major urban district in northwest Beijing known for its universities, technology hubs, and historic imperial gardens.
-
D.
北京皇城
北京皇城是明清两代北京城中围绕皇宫设置的内城区域,汇集重要宫殿、坛庙和皇家建筑群的核心防御与礼制空间。
-
E.
Dongcheng District, Beijing
Dongcheng District, Beijing is a central urban district of China's capital city that encompasses many of its most important political, historical, and cultural landmarks, including Tiananmen Square and the Forbidden City.
- 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_69d6aa5b0d2881909584b20efc5877f0 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6f861513881909b44c711371086b7 |
completed | April 9, 2026, 12:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d98865f700819093c8cadc6fcef75f |
completed | April 10, 2026, 11:31 p.m. |
| NEDg | Description generation | batch_69d98ae8403c81908a229aa06bd0388a |
completed | April 10, 2026, 11:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d98ce9ba0c8190a7c62fa670e23705 |
completed | April 10, 2026, 11:51 p.m. |
Created at: April 8, 2026, 9:09 p.m.