Triple

T10628707
Position Surface form Disambiguated ID Type / Status
Subject Xiaoting District E250391 entity
Predicate hasNameInChinese P4878 FINISHED
Object 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.
 E875725 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: 猇亭区 | Statement: [Xiaoting District, hasNameInChinese, 猇亭区]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 猇亭区
Context triple: [Xiaoting District, hasNameInChinese, 猇亭区]
  • A. 黄石港区
    黄石港区 is an urban district of Huangshi City in Hubei Province, China, known as one of the city’s central administrative and commercial areas along the Yangtze River.
  • B. 黄州区
    黄州区 is an urban district under the jurisdiction of Huanggang City in Hubei Province, China, known as its political, economic, and cultural center.
  • C. 茅箭区
    茅箭区 is an urban district of Shiyan City in Hubei Province, China, known as a central administrative and commercial area.
  • D. 汉阳
    汉阳是中国湖北省武汉市的一个历史悠久的城区，位于长江与汉江交汇处，以其工业基础和文化遗产而闻名。
  • E. 下陆区
    下陆区 is an urban district of Huangshi City in Hubei Province, China, known for its industrial base and role in the city’s economic development.
  • 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: 猇亭区
Triple: [Xiaoting District, hasNameInChinese, 猇亭区]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 猇亭区
Target entity description: 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.
  • A. 黄石港区
    黄石港区 is an urban district of Huangshi City in Hubei Province, China, known as one of the city’s central administrative and commercial areas along the Yangtze River.
  • B. 黄州区
    黄州区 is an urban district under the jurisdiction of Huanggang City in Hubei Province, China, known as its political, economic, and cultural center.
  • C. 茅箭区
    茅箭区 is an urban district of Shiyan City in Hubei Province, China, known as a central administrative and commercial area.
  • D. 汉阳
    汉阳是中国湖北省武汉市的一个历史悠久的城区，位于长江与汉江交汇处，以其工业基础和文化遗产而闻名。
  • E. 下陆区
    下陆区 is an urban district of Huangshi City in Hubei Province, China, known for its industrial base and role in the city’s economic development.
  • 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_69d6aa5993448190a493b790b8f85010 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6df92f8388190a8bcff96809d8eb4 completed April 8, 2026, 11:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69d96babc290819096c0c914d038ba01 completed April 10, 2026, 9:29 p.m.
NEDg Description generation batch_69d96def8bfc81909d6a5addf724691b completed April 10, 2026, 9:38 p.m.
NED2 Entity disambiguation (via description) batch_69d96fedb18881908570593856f4aade completed April 10, 2026, 9:47 p.m.
Created at: April 8, 2026, 8:59 p.m.