Triple

T16513588
Position Surface form Disambiguated ID Type / Status
Subject 六安 E401123 entity
Predicate borderedBy P224 FINISHED
Object 安庆市
安庆市 is a prefecture-level city in southwestern Anhui Province, China, known as a historic cultural center along the Yangtze River.
 E1221391 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: 安庆市 | Statement: [六安, borderedBy, 安庆市]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 安庆市
Context triple: [六安, borderedBy, 安庆市]
  • A. 合肥市
    合肥市 is the capital and largest city of Anhui Province in eastern China, known as a major political, economic, and technological center in the region.
  • B. Santarém
    Santarém is a Brazilian city in the state of Pará, known for its location at the confluence of the Amazon and Tapajós rivers and its striking “meeting of the waters” phenomenon.
  • C. Santarém
    Santarém is a historic Portuguese city in the Ribatejo region, known for its Gothic architecture and strategic position overlooking the Tagus River.
  • D. Rio Branco-ES
    Rio Branco-ES is a Brazilian football club based in the state of Espírito Santo, known for competing in regional and lower-division national competitions.
  • E. Três Lagoas
    Três Lagoas is a Brazilian city in the state of Mato Grosso do Sul known for its strong pulp and paper industry and growing industrial sector.
  • 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: 安庆市
Triple: [六安, borderedBy, 安庆市]
Generated description
安庆市 is a prefecture-level city in southwestern Anhui Province, China, known as a historic cultural center along the Yangtze River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 安庆市
Target entity description: 安庆市 is a prefecture-level city in southwestern Anhui Province, China, known as a historic cultural center along the Yangtze River.
  • A. 合肥市
    合肥市 is the capital and largest city of Anhui Province in eastern China, known as a major political, economic, and technological center in the region.
  • B. Santarém
    Santarém is a Brazilian city in the state of Pará, known for its location at the confluence of the Amazon and Tapajós rivers and its striking “meeting of the waters” phenomenon.
  • C. Santarém
    Santarém is a historic Portuguese city in the Ribatejo region, known for its Gothic architecture and strategic position overlooking the Tagus River.
  • D. Rio Branco-ES
    Rio Branco-ES is a Brazilian football club based in the state of Espírito Santo, known for competing in regional and lower-division national competitions.
  • E. Três Lagoas
    Três Lagoas is a Brazilian city in the state of Mato Grosso do Sul known for its strong pulp and paper industry and growing industrial sector.
  • 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_69d88381f6148190819958a038be990e completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e32e78d4848190a55de9902115b1b2 completed April 18, 2026, 7:10 a.m.
NED1 Entity disambiguation (via context triple) batch_6a006ed6627081909dae6b4259a5609c completed May 10, 2026, 11:41 a.m.
NEDg Description generation batch_6a006fad87d481908d20e15392e6bdc9 completed May 10, 2026, 11:44 a.m.
NED2 Entity disambiguation (via description) batch_6a007088fd988190b3dfef081769d03e completed May 10, 2026, 11:48 a.m.
Created at: April 10, 2026, 5:14 a.m.