Triple

T15080387
Position Surface form Disambiguated ID Type / Status
Subject Weiße Elster E380123 entity
Predicate tributary P415 FINISHED
Object Weida
Weida is a river in eastern Germany that serves as a significant tributary of the Weiße Elster.
 E1136418 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: Weida | Statement: [Weiße Elster, tributary, Weida]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Weida
Context triple: [Weiße Elster, tributary, Weida]
  • A. Luzhi
    Luzhi is an ancient canal town near Suzhou in China, renowned for its well-preserved waterways, stone bridges, and traditional Jiangnan architecture.
  • B. Shancheng
    Shancheng is a Chinese nickname meaning "Mountain City," commonly used to refer to the city of Chongqing, known for its steep terrain and hilly urban landscape.
  • C. Jianye
    Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
  • D. Jinyang
    Jinyang is the historical name of the city now known as Taiyuan, a major urban and industrial center in northern China’s Shanxi province.
  • E. Dunhua
    Dunhua is a county-level city in northeastern China's Jilin Province, known for its location within the Yanbian Korean Autonomous Prefecture and its mix of Han and Korean cultural influences.
  • 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: Weida
Triple: [Weiße Elster, tributary, Weida]
Generated description
Weida is a river in eastern Germany that serves as a significant tributary of the Weiße Elster.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Weida
Target entity description: Weida is a river in eastern Germany that serves as a significant tributary of the Weiße Elster.
  • A. Luzhi
    Luzhi is an ancient canal town near Suzhou in China, renowned for its well-preserved waterways, stone bridges, and traditional Jiangnan architecture.
  • B. Shancheng
    Shancheng is a Chinese nickname meaning "Mountain City," commonly used to refer to the city of Chongqing, known for its steep terrain and hilly urban landscape.
  • C. Jianye
    Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
  • D. Jinyang
    Jinyang is the historical name of the city now known as Taiyuan, a major urban and industrial center in northern China’s Shanxi province.
  • E. Dunhua
    Dunhua is a county-level city in northeastern China's Jilin Province, known for its location within the Yanbian Korean Autonomous Prefecture and its mix of Han and Korean cultural influences.
  • 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_69d85cd7683881908d405c1b5d7b4f7f completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69dff80008c88190840f94222f867478 completed April 15, 2026, 8:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69feae15d6308190a62b4f66c550db04 completed May 9, 2026, 3:46 a.m.
NEDg Description generation batch_69feaf8e1b508190b0b5ceb64d44fad6 completed May 9, 2026, 3:52 a.m.
NED2 Entity disambiguation (via description) batch_69feb038065c8190b60266644db64092 completed May 9, 2026, 3:55 a.m.
Created at: April 10, 2026, 3:03 a.m.