Triple

T498583
Position Surface form Disambiguated ID Type / Status
Subject North China E10349 entity
Predicate containsMajorCity P316 FINISHED
Object Taiyuan
Taiyuan is the capital and largest city of Shanxi Province in northern China, known as an important industrial and transportation hub with a long imperial history.
 E80116 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: Taiyuan | Statement: [North China, containsMajorCity, Taiyuan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Taiyuan
Context triple: [North China, containsMajorCity, Taiyuan]
  • A. Shijiazhuang
    Shijiazhuang is the capital and largest city of Hebei Province in northern China, known as a major industrial and transportation hub.
  • B. Tianjin
    Tianjin is a major port city and industrial hub in northern China, located near Beijing along the Bohai Sea.
  • C. Beijing
    Beijing is the capital city of China, a major political, cultural, and economic center known for its rich history and rapid modern development.
  • D. Lanzhou
    Lanzhou is a major city in northwestern China and the capital of Gansu Province, known historically as a key hub on the ancient Silk Road.
  • E. Shenyang
    Shenyang is a major industrial and historical city in northeastern China and the capital of Liaoning Province.
  • 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: Taiyuan
Triple: [North China, containsMajorCity, Taiyuan]
Generated description
Taiyuan is the capital and largest city of Shanxi Province in northern China, known as an important industrial and transportation hub with a long imperial history.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Taiyuan
Target entity description: Taiyuan is the capital and largest city of Shanxi Province in northern China, known as an important industrial and transportation hub with a long imperial history.
  • A. Shijiazhuang
    Shijiazhuang is the capital and largest city of Hebei Province in northern China, known as a major industrial and transportation hub.
  • B. Tianjin
    Tianjin is a major port city and industrial hub in northern China, located near Beijing along the Bohai Sea.
  • C. Beijing
    Beijing is the capital city of China, a major political, cultural, and economic center known for its rich history and rapid modern development.
  • D. Lanzhou
    Lanzhou is a major city in northwestern China and the capital of Gansu Province, known historically as a key hub on the ancient Silk Road.
  • E. Shenyang
    Shenyang is a major industrial and historical city in northeastern China and the capital of Liaoning Province.
  • 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_69a2e847df8481909239ec08ccf1e376 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2f1183e988190bce70932a9678134 completed Feb. 28, 2026, 1:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69a573ff0574819083e39dc93de4311f completed March 2, 2026, 11:26 a.m.
NEDg Description generation batch_69a574cd4970819085e97d86e47d2e7b completed March 2, 2026, 11:30 a.m.
NED2 Entity disambiguation (via description) batch_69a57686f7dc8190a94e0f36cc47fa3a completed March 2, 2026, 11:37 a.m.
Created at: Feb. 28, 2026, 1:12 p.m.