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.