Triple

T1810543
Position Surface form Disambiguated ID Type / Status
Subject Long Biên District E40320 entity
Predicate hasWard P14475 FINISHED
Object Gia Thụy
Gia Thụy is an urban ward located within Long Biên District of Hanoi, Vietnam.
 E202482 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: Gia Thụy | Statement: [Long Biên District, hasWard, Gia Thụy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gia Thụy
Context triple: [Long Biên District, hasWard, Gia Thụy]
  • A. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • B. Lingang
    Lingang is a rapidly developing industrial and high-tech district in Shanghai, China, known for hosting major manufacturing facilities such as Tesla’s Gigafactory Shanghai.
  • C. Jianye
    Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
  • D. Luyang
    Luyang is a historic name associated with the city of Hefei, the capital of Anhui Province in eastern China.
  • E. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • 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: Gia Thụy
Triple: [Long Biên District, hasWard, Gia Thụy]
Generated description
Gia Thụy is an urban ward located within Long Biên District of Hanoi, Vietnam.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Gia Thụy
Target entity description: Gia Thụy is an urban ward located within Long Biên District of Hanoi, Vietnam.
  • A. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • B. Lingang
    Lingang is a rapidly developing industrial and high-tech district in Shanghai, China, known for hosting major manufacturing facilities such as Tesla’s Gigafactory Shanghai.
  • C. Jianye
    Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
  • D. Luyang
    Luyang is a historic name associated with the city of Hefei, the capital of Anhui Province in eastern China.
  • E. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • 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_69a88643a3388190a612f2ebe1fb29e7 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa65c461e881908070cb80d9092981 completed March 6, 2026, 5:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69adb5e352d88190839cde25e3c07d95 completed March 8, 2026, 5:46 p.m.
NEDg Description generation batch_69adb8b6d160819096dc02323049101d completed March 8, 2026, 5:58 p.m.
NED2 Entity disambiguation (via description) batch_69adb9bafd688190a66a835c6a8163e3 completed March 8, 2026, 6:02 p.m.
Created at: March 4, 2026, 7:32 p.m.