Triple

T20402195
Position Surface form Disambiguated ID Type / Status
Subject Amazonas Region E500361 entity
Predicate hasCity P316 FINISHED
Object Bagua
Bagua is a city in northern Peru’s Amazonas Region known as a commercial and transport hub between the Andean highlands and the Amazon rainforest.
 E1428214 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: Bagua | Statement: [Amazonas Region, hasCity, Bagua]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bagua
Context triple: [Amazonas Region, hasCity, Bagua]
  • A. Yangluo
    Yangluo is a town in Wuhan, Hubei Province, China, known as an industrial and port area along the Yangtze River.
  • B. Qiaoban
    Qiaoban is a Chinese government agency under the State Council responsible for managing affairs related to overseas Chinese and their ties to China.
  • C. Zhiyan
    Zhiyan was an influential Chinese Buddhist monk and early Huayan school patriarch whose teachings shaped the thought of later Korean monk Uisang.
  • D. Gezhouba
    Gezhouba is a locality on the Yangtze River in Yichang, Hubei Province, China, known primarily as the site of the Gezhouba Dam hydropower project.
  • E. Bagasara
    Bagasara is a town in the Amreli district of Gujarat, India, known for its local commerce and regional cultural significance.
  • 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: Bagua
Triple: [Amazonas Region, hasCity, Bagua]
Generated description
Bagua is a city in northern Peru’s Amazonas Region known as a commercial and transport hub between the Andean highlands and the Amazon rainforest.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bagua
Target entity description: Bagua is a city in northern Peru’s Amazonas Region known as a commercial and transport hub between the Andean highlands and the Amazon rainforest.
  • A. Yangluo
    Yangluo is a town in Wuhan, Hubei Province, China, known as an industrial and port area along the Yangtze River.
  • B. Qiaoban
    Qiaoban is a Chinese government agency under the State Council responsible for managing affairs related to overseas Chinese and their ties to China.
  • C. Zhiyan
    Zhiyan was an influential Chinese Buddhist monk and early Huayan school patriarch whose teachings shaped the thought of later Korean monk Uisang.
  • D. Gezhouba
    Gezhouba is a locality on the Yangtze River in Yichang, Hubei Province, China, known primarily as the site of the Gezhouba Dam hydropower project.
  • E. Bagasara
    Bagasara is a town in the Amreli district of Gujarat, India, known for its local commerce and regional cultural significance.
  • 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_69e0b4a81bec8190b69adfdc1336a015 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6798fc3b88190a372c34102bfaa6f completed April 20, 2026, 7:07 p.m.
NED1 Entity disambiguation (via context triple) batch_6a08762bd7e881909ceddb1b3c26a914 completed May 16, 2026, 1:50 p.m.
NEDg Description generation batch_6a0877b7ed888190bccc1ba665528aca completed May 16, 2026, 1:57 p.m.
NED2 Entity disambiguation (via description) batch_6a087834ea1c8190b1b94c70a807668a completed May 16, 2026, 1:59 p.m.
Created at: April 16, 2026, 11:29 a.m.