Triple

T13851117
Position Surface form Disambiguated ID Type / Status
Subject Shuozhou E332942 entity
Predicate hasChineseName P4878 FINISHED
Object 朔州
朔州是位于中国山西省北部的一座地级市，以煤炭资源丰富和历史文化遗迹著称。
 E1065875 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: 朔州 | Statement: [Shuozhou, hasChineseName, 朔州]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 朔州
Context triple: [Shuozhou, hasChineseName, 朔州]
  • A. Winster
    Winster is a historic village in England’s Peak District, known for its traditional stone houses, former lead-mining heritage, and well-preserved conservation area.
  • B. Swansea
    Swansea is a coastal city in South Wales known for its maritime heritage, industrial history, and role as a target during World War II air raids.
  • C. Swansea
    Swansea is a coastal town in Bristol County, Massachusetts, known for its suburban character and proximity to both Providence and Fall River.
  • D. Exeter
    Exeter is a historic cathedral city in Devon, England, known for its medieval architecture and role as a regional administrative and cultural center.
  • E. Exeter
    Exeter is a historic town in Rockingham County, New Hampshire, known for its colonial heritage and as the home of the prestigious Phillips Exeter Academy.
  • 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: 朔州
Triple: [Shuozhou, hasChineseName, 朔州]
Generated description
朔州是位于中国山西省北部的一座地级市，以煤炭资源丰富和历史文化遗迹著称。
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 朔州
Target entity description: 朔州是位于中国山西省北部的一座地级市，以煤炭资源丰富和历史文化遗迹著称。
  • A. Winster
    Winster is a historic village in England’s Peak District, known for its traditional stone houses, former lead-mining heritage, and well-preserved conservation area.
  • B. Swansea
    Swansea is a coastal city in South Wales known for its maritime heritage, industrial history, and role as a target during World War II air raids.
  • C. Swansea
    Swansea is a coastal town in Bristol County, Massachusetts, known for its suburban character and proximity to both Providence and Fall River.
  • D. Exeter
    Exeter is a historic cathedral city in Devon, England, known for its medieval architecture and role as a regional administrative and cultural center.
  • E. Exeter
    Exeter is a historic town in Rockingham County, New Hampshire, known for its colonial heritage and as the home of the prestigious Phillips Exeter Academy.
  • 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_69d81c5ba13c8190839315f54768acfd completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de02d8fb788190baef7537be2baecb completed April 14, 2026, 9:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7c0f73838819085d6f052c00fc494 completed May 3, 2026, 9:41 p.m.
NEDg Description generation batch_69f7c23410dc8190972245cecfa85871 completed May 3, 2026, 9:46 p.m.
NED2 Entity disambiguation (via description) batch_69f7c3031a0c8190ad30d59fe1f2322e completed May 3, 2026, 9:49 p.m.
Created at: April 9, 2026, 10:14 p.m.