Triple

T6007325
Position Surface form Disambiguated ID Type / Status
Subject Ashanti Region E133743 entity
Predicate hasMajorCity P316 FINISHED
Object Konongo
Konongo is a prominent mining and commercial town in central Ghana known historically for its gold deposits and later manganese production.
 E561830 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: Konongo | Statement: [Ashanti Region, hasMajorCity, Konongo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Konongo
Context triple: [Ashanti Region, hasMajorCity, Konongo]
  • A. Kongō
    Kongō was a Japanese Kongō-class fast battleship that served prominently in the Imperial Japanese Navy during World War II.
  • B. Luba-Kasai
    Luba-Kasai is a Bantu language spoken primarily in the Kasai region of the Democratic Republic of the Congo by the Luba people.
  • C. Kongo
    Kongo refers to the Central African ethnic and cultural group and historical kingdom whose traditions and beliefs have significantly influenced Afro-diasporic religions in the Americas.
  • D. Ongwediva
    Ongwediva is a growing town in northern Namibia known as an educational and commercial hub, hosting institutions like the University of Namibia’s campus and the annual Ongwediva Trade Fair.
  • E. Congo
    Congo is a Central African country whose economy is heavily reliant on oil production and exports.
  • 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: Konongo
Triple: [Ashanti Region, hasMajorCity, Konongo]
Generated description
Konongo is a prominent mining and commercial town in central Ghana known historically for its gold deposits and later manganese production.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Konongo
Target entity description: Konongo is a prominent mining and commercial town in central Ghana known historically for its gold deposits and later manganese production.
  • A. Kongō
    Kongō was a Japanese Kongō-class fast battleship that served prominently in the Imperial Japanese Navy during World War II.
  • B. Luba-Kasai
    Luba-Kasai is a Bantu language spoken primarily in the Kasai region of the Democratic Republic of the Congo by the Luba people.
  • C. Kongo
    Kongo refers to the Central African ethnic and cultural group and historical kingdom whose traditions and beliefs have significantly influenced Afro-diasporic religions in the Americas.
  • D. Ongwediva
    Ongwediva is a growing town in northern Namibia known as an educational and commercial hub, hosting institutions like the University of Namibia’s campus and the annual Ongwediva Trade Fair.
  • E. Congo
    Congo is a Central African country whose economy is heavily reliant on oil production and exports.
  • 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_69c00872444c8190bfaf1739dcec765c completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c04f13d9908190a11d9bef8652db93 completed March 22, 2026, 8:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c10895559081908b9efdd32ecef37f completed March 23, 2026, 9:32 a.m.
NEDg Description generation batch_69c10b7467e88190955014bc060b20e4 completed March 23, 2026, 9:44 a.m.
NED2 Entity disambiguation (via description) batch_69c10c0a001c81908e3ca53e9491ff9a completed March 23, 2026, 9:46 a.m.
Created at: March 22, 2026, 4:06 p.m.