Triple

T3109978
Position Surface form Disambiguated ID Type / Status
Subject Shanxi Province E64927 entity
Predicate hasMajorCity P316 FINISHED
Object Jincheng
Jincheng is a prefecture-level city in southeastern Shanxi Province, China, known for its coal resources and heavy industry.
 E332941 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: Jincheng | Statement: [Shanxi Province, hasMajorCity, Jincheng]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jincheng
Context triple: [Shanxi Province, hasMajorCity, Jincheng]
  • A. Lincang
    Lincang is a prefecture-level city in southwestern China known for its tea production, diverse ethnic cultures, and location near the border with Myanmar.
  • 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. Hucheng
    Hucheng is the given name of Yang Hucheng, a prominent Chinese general and political figure best known for his role in the Xi'an Incident of 1936.
  • D. Xinjing
    Xinjing was the capital city of the Japanese puppet state of Manchukuo in northeastern China during the 1930s and early 1940s.
  • E. Shëngjin
    Shëngjin is a coastal town and port in northwestern Albania on the Adriatic Sea, historically significant for its strategic maritime position.
  • 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: Jincheng
Triple: [Shanxi Province, hasMajorCity, Jincheng]
Generated description
Jincheng is a prefecture-level city in southeastern Shanxi Province, China, known for its coal resources and heavy industry.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jincheng
Target entity description: Jincheng is a prefecture-level city in southeastern Shanxi Province, China, known for its coal resources and heavy industry.
  • A. Lincang
    Lincang is a prefecture-level city in southwestern China known for its tea production, diverse ethnic cultures, and location near the border with Myanmar.
  • 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. Hucheng
    Hucheng is the given name of Yang Hucheng, a prominent Chinese general and political figure best known for his role in the Xi'an Incident of 1936.
  • D. Xinjing
    Xinjing was the capital city of the Japanese puppet state of Manchukuo in northeastern China during the 1930s and early 1940s.
  • E. Shëngjin
    Shëngjin is a coastal town and port in northwestern Albania on the Adriatic Sea, historically significant for its strategic maritime position.
  • 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_69b235a8949881908496a1413ffa2658 completed March 12, 2026, 3:40 a.m.
NEDg Description generation batch_69b236962ee48190b37836e5fe6dbc37 completed March 12, 2026, 3:44 a.m.
NED2 Entity disambiguation (via description) batch_69b237397e14819093a7192d28c59ad1 completed March 12, 2026, 3:47 a.m.
Created at: March 8, 2026, 3:04 p.m.