Triple

T8113115
Position Surface form Disambiguated ID Type / Status
Subject Liwan District E189403 entity
Predicate hasChineseName P4878 FINISHED
Object 荔湾区
荔湾区 is a historic urban district of Guangzhou, China, known for its traditional Lingnan architecture, cultural heritage sites, and vibrant commercial streets.
 E712901 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: 荔湾区 | Statement: [Liwan District, hasChineseName, 荔湾区]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 荔湾区
Context triple: [Liwan District, hasChineseName, 荔湾区]
  • A. 沙面岛
    沙面岛是位于中国广州珠江中的一座历史文化小岛，以其保存完好的欧陆风情建筑群和曾为多国租界的独特殖民历史而闻名。
  • B. Baiyun District
    Baiyun District is a major urban district of Guangzhou, China, known for hosting the city’s primary international airport and serving as a key transportation and industrial hub.
  • C. Huangpu District, Guangzhou
    Huangpu District, Guangzhou is a historic urban district of Guangzhou, China, best known as the site of the influential Whampoa Military Academy and now a key industrial and port area of the city.
  • D. Xiangzhou District
    Xiangzhou District is the central urban district of Zhuhai in Guangdong Province, China, known for its government, commercial, and coastal areas facing Macau.
  • E. 东区
    东区是韩国蔚山广域市的一个行政区，以工业设施与港口经济为主。
  • 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: 荔湾区
Triple: [Liwan District, hasChineseName, 荔湾区]
Generated description
荔湾区 is a historic urban district of Guangzhou, China, known for its traditional Lingnan architecture, cultural heritage sites, and vibrant commercial streets.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 荔湾区
Target entity description: 荔湾区 is a historic urban district of Guangzhou, China, known for its traditional Lingnan architecture, cultural heritage sites, and vibrant commercial streets.
  • A. 沙面岛
    沙面岛是位于中国广州珠江中的一座历史文化小岛，以其保存完好的欧陆风情建筑群和曾为多国租界的独特殖民历史而闻名。
  • B. Baiyun District
    Baiyun District is a major urban district of Guangzhou, China, known for hosting the city’s primary international airport and serving as a key transportation and industrial hub.
  • C. Huangpu District, Guangzhou
    Huangpu District, Guangzhou is a historic urban district of Guangzhou, China, best known as the site of the influential Whampoa Military Academy and now a key industrial and port area of the city.
  • D. Xiangzhou District
    Xiangzhou District is the central urban district of Zhuhai in Guangdong Province, China, known for its government, commercial, and coastal areas facing Macau.
  • E. 东区
    东区是韩国蔚山广域市的一个行政区，以工业设施与港口经济为主。
  • 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_69ca82baad008190ab2859712b9b1607 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb432d7dfc8190b9c980f32c7b4623 completed March 31, 2026, 3:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc9433a5848190aac09a2589061b53 completed April 1, 2026, 3:42 a.m.
NEDg Description generation batch_69cc963fe2f8819098ad6a726e226189 completed April 1, 2026, 3:51 a.m.
NED2 Entity disambiguation (via description) batch_69cc97781c0481909d21293633d111be completed April 1, 2026, 3:56 a.m.
Created at: March 30, 2026, 5:32 p.m.