Triple

T3109981
Position Surface form Disambiguated ID Type / Status
Subject Shanxi Province E64927 entity
Predicate hasMajorCity P316 FINISHED
Object Jinzhong
Jinzhong is a prefecture-level city in northern China known for its historical sites and cultural heritage within Shanxi Province.
 E336863 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: Jinzhong | Statement: [Shanxi Province, hasMajorCity, Jinzhong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jinzhong
Context triple: [Shanxi Province, hasMajorCity, Jinzhong]
  • A. 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.
  • 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. Jincheng
    Jincheng is a prefecture-level city in southeastern Shanxi Province, China, known for its coal resources and heavy industry.
  • D. Linfen
    Linfen is a major industrial city in southern Shanxi Province, China, historically known for coal production and severe air pollution.
  • E. Shuozhou
    Shuozhou is a prefecture-level city in northern China known for its coal resources and historical sites within Shanxi 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: Jinzhong
Triple: [Shanxi Province, hasMajorCity, Jinzhong]
Generated description
Jinzhong is a prefecture-level city in northern China known for its historical sites and cultural heritage within Shanxi Province.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jinzhong
Target entity description: Jinzhong is a prefecture-level city in northern China known for its historical sites and cultural heritage within Shanxi Province.
  • A. 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.
  • 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. Jincheng
    Jincheng is a prefecture-level city in southeastern Shanxi Province, China, known for its coal resources and heavy industry.
  • D. Linfen
    Linfen is a major industrial city in southern Shanxi Province, China, historically known for coal production and severe air pollution.
  • E. Shuozhou
    Shuozhou is a prefecture-level city in northern China known for its coal resources and historical sites within Shanxi 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_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_69b261eb2e708190b192574d3f5862e6 completed March 12, 2026, 6:49 a.m.
NEDg Description generation batch_69b2638b3b2881909563356ea8a9611c completed March 12, 2026, 6:56 a.m.
NED2 Entity disambiguation (via description) batch_69b264fb42e4819084c289235f33b654 completed March 12, 2026, 7:02 a.m.
Created at: March 8, 2026, 3:04 p.m.