Triple

T8186637
Position Surface form Disambiguated ID Type / Status
Subject Dalian E191200 entity
Predicate hasCountyLevelCity P27799 FINISHED
Object Wafangdian
Wafangdian is a county-level city in Liaoning Province, China, known for its bearing industry and as an important satellite city of Dalian.
 E758734 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: Wafangdian | Statement: [Dalian, hasCountyLevelCity, Wafangdian]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wafangdian
Context triple: [Dalian, hasCountyLevelCity, Wafangdian]
  • A. Lüshunkou
    Lüshunkou is a strategically important port city at the tip of the Liaodong Peninsula in northeastern China, historically known as Port Arthur and the site of major naval and military conflicts.
  • B. Wudaokou
    Wudaokou is a bustling neighborhood in Beijing known for its universities, tech companies, and vibrant student nightlife.
  • C. Licheng
    Licheng is the courtesy name of the Daoguang Emperor, a Qing dynasty ruler of China in the early 19th century.
  • D. Wuyuan
    Wuyuan is a historic county in northeastern Jiangxi, China, famed for its well-preserved Huizhou-style architecture and picturesque rural landscapes.
  • E. Zhushikou
    Zhushikou is a subway station on the Beijing Subway system serving the central area near Beijing’s historic old city.
  • 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: Wafangdian
Triple: [Dalian, hasCountyLevelCity, Wafangdian]
Generated description
Wafangdian is a county-level city in Liaoning Province, China, known for its bearing industry and as an important satellite city of Dalian.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wafangdian
Target entity description: Wafangdian is a county-level city in Liaoning Province, China, known for its bearing industry and as an important satellite city of Dalian.
  • A. Lüshunkou
    Lüshunkou is a strategically important port city at the tip of the Liaodong Peninsula in northeastern China, historically known as Port Arthur and the site of major naval and military conflicts.
  • B. Wudaokou
    Wudaokou is a bustling neighborhood in Beijing known for its universities, tech companies, and vibrant student nightlife.
  • C. Licheng
    Licheng is the courtesy name of the Daoguang Emperor, a Qing dynasty ruler of China in the early 19th century.
  • D. Wuyuan
    Wuyuan is a historic county in northeastern Jiangxi, China, famed for its well-preserved Huizhou-style architecture and picturesque rural landscapes.
  • E. Zhushikou
    Zhushikou is a subway station on the Beijing Subway system serving the central area near Beijing’s historic old city.
  • 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_69ca82c5b6948190a583c096fb0a6c71 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb4d9e01208190842170abf62d9afb completed March 31, 2026, 4:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69cf6e5358888190ad1b5771ca00a097 completed April 3, 2026, 7:37 a.m.
NEDg Description generation batch_69cf7041b6bc81909924d1382b756746 completed April 3, 2026, 7:46 a.m.
NED2 Entity disambiguation (via description) batch_69cf714f22c48190a192c6fc32debfd6 completed April 3, 2026, 7:50 a.m.
Created at: March 30, 2026, 5:41 p.m.