Triple

T4825731
Position Surface form Disambiguated ID Type / Status
Subject Suzhou E107819 entity
Predicate hasWaterTown P59883 FINISHED
Object Luzhi
Luzhi is an ancient canal town near Suzhou in China, renowned for its well-preserved waterways, stone bridges, and traditional Jiangnan architecture.
 E475480 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: Luzhi | Statement: [Suzhou, hasWaterTown, Luzhi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Luzhi
Context triple: [Suzhou, hasWaterTown, Luzhi]
  • A. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • B. Jianye
    Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
  • C. Lüshun
    Lüshun is a strategically important port city in northeastern China, historically known as Port Arthur and noted for its role in several major conflicts.
  • D. Ruchang
    Ruchang is a Chinese given name most notably borne by Ding Ruchang, a late Qing dynasty naval commander.
  • E. Lüliang
    Lüliang is a prefecture-level city in western Shanxi Province, China, known for its mountainous terrain and significant coal and energy resources.
  • 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: Luzhi
Triple: [Suzhou, hasWaterTown, Luzhi]
Generated description
Luzhi is an ancient canal town near Suzhou in China, renowned for its well-preserved waterways, stone bridges, and traditional Jiangnan architecture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Luzhi
Target entity description: Luzhi is an ancient canal town near Suzhou in China, renowned for its well-preserved waterways, stone bridges, and traditional Jiangnan architecture.
  • A. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • B. Jianye
    Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
  • C. Lüshun
    Lüshun is a strategically important port city in northeastern China, historically known as Port Arthur and noted for its role in several major conflicts.
  • D. Ruchang
    Ruchang is a Chinese given name most notably borne by Ding Ruchang, a late Qing dynasty naval commander.
  • E. Lüliang
    Lüliang is a prefecture-level city in western Shanxi Province, China, known for its mountainous terrain and significant coal and energy resources.
  • 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_69bd43fac8188190803f0327190621e4 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd7162427c81908a67a07545f698ae completed March 20, 2026, 4:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69be5cb748d081908fc32b2cea994b35 completed March 21, 2026, 8:54 a.m.
NEDg Description generation batch_69be607df6648190be22b5bc0d6531b4 completed March 21, 2026, 9:10 a.m.
NED2 Entity disambiguation (via description) batch_69be611da7c08190b644cfbcb30741fc completed March 21, 2026, 9:13 a.m.
Created at: March 20, 2026, 1:24 p.m.