Triple

T3109982
Position Surface form Disambiguated ID Type / Status
Subject Shanxi Province E64927 entity
Predicate hasMajorCity P316 FINISHED
Object Xinzhou
Xinzhou is a prefecture-level city in northern China known for its historical sites and location within Shanxi Province’s coal-rich and culturally significant region.
 E339987 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: Xinzhou | Statement: [Shanxi Province, hasMajorCity, Xinzhou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Xinzhou
Context triple: [Shanxi Province, hasMajorCity, Xinzhou]
  • A. Jinzhong
    Jinzhong is a prefecture-level city in northern China known for its historical sites and cultural heritage within Shanxi Province.
  • B. Datong
    Datong is a historic industrial city in northern China known for its coal production and nearby cultural landmarks such as the Yungang Grottoes.
  • C. 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.
  • D. Shuozhou
    Shuozhou is a prefecture-level city in northern China known for its coal resources and historical sites within Shanxi Province.
  • E. Jincheng
    Jincheng is a prefecture-level city in southeastern Shanxi Province, China, known for its coal resources and heavy industry.
  • 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: Xinzhou
Triple: [Shanxi Province, hasMajorCity, Xinzhou]
Generated description
Xinzhou is a prefecture-level city in northern China known for its historical sites and location within Shanxi Province’s coal-rich and culturally significant region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Xinzhou
Target entity description: Xinzhou is a prefecture-level city in northern China known for its historical sites and location within Shanxi Province’s coal-rich and culturally significant region.
  • A. Jinzhong
    Jinzhong is a prefecture-level city in northern China known for its historical sites and cultural heritage within Shanxi Province.
  • B. Datong
    Datong is a historic industrial city in northern China known for its coal production and nearby cultural landmarks such as the Yungang Grottoes.
  • C. 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.
  • D. Shuozhou
    Shuozhou is a prefecture-level city in northern China known for its coal resources and historical sites within Shanxi Province.
  • E. Jincheng
    Jincheng is a prefecture-level city in southeastern Shanxi Province, China, known for its coal resources and heavy industry.
  • 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_69ad857eeaf48190b34ebfdaa7a264cf completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada2a0ab2481908db50738ec3ad0fb completed March 8, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69b276df42048190bcb79277a28f866a completed March 12, 2026, 8:18 a.m.
NEDg Description generation batch_69b27ab4bcb081909a59f41449f7bde8 completed March 12, 2026, 8:35 a.m.
NED2 Entity disambiguation (via description) batch_69b27b1ab6848190bebc7b02ce67071c completed March 12, 2026, 8:36 a.m.
Created at: March 8, 2026, 3:04 p.m.