Triple

T2566120
Position Surface form Disambiguated ID Type / Status
Subject Kikongo E57354 entity
Predicate hasDialect P4251 FINISHED
Object Kisolongo
Kisolongo is a regional dialect of the Kikongo language spoken by Kongo communities in parts of Central Africa.
 E279950 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: Kisolongo | Statement: [Kikongo, hasDialect, Kisolongo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kisolongo
Context triple: [Kikongo, hasDialect, Kisolongo]
  • A. Kololo
    Kololo refers to the Sotho-speaking people whose 19th-century migration and conquest in south-central Africa significantly influenced the formation and language of the Lozi kingdom in present-day Zambia.
  • B. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • C. Mikongo
    Mikongo is a small settlement in central Gabon that serves as a key access point for visitors exploring Lope National Park.
  • D. Chambo
    Chambo is a small town in central Ecuador known for its agricultural activities and proximity to the Andean highlands.
  • E. Sanglechi
    Sanglechi is a lesser-known Eastern Iranian language spoken in parts of northeastern Afghanistan and adjacent regions.
  • 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: Kisolongo
Triple: [Kikongo, hasDialect, Kisolongo]
Generated description
Kisolongo is a regional dialect of the Kikongo language spoken by Kongo communities in parts of Central Africa.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kisolongo
Target entity description: Kisolongo is a regional dialect of the Kikongo language spoken by Kongo communities in parts of Central Africa.
  • A. Kololo
    Kololo refers to the Sotho-speaking people whose 19th-century migration and conquest in south-central Africa significantly influenced the formation and language of the Lozi kingdom in present-day Zambia.
  • B. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • C. Mikongo
    Mikongo is a small settlement in central Gabon that serves as a key access point for visitors exploring Lope National Park.
  • D. Chambo
    Chambo is a small town in central Ecuador known for its agricultural activities and proximity to the Andean highlands.
  • E. Sanglechi
    Sanglechi is a lesser-known Eastern Iranian language spoken in parts of northeastern Afghanistan and adjacent regions.
  • 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_69ab4a4ef9008190a0e6d4422b9418b7 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd35ef22c8190966612cc75f69eca completed March 7, 2026, 7:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69af6562d6f08190a2be483b06a789cc completed March 10, 2026, 12:27 a.m.
NEDg Description generation batch_69af669436208190901d1f34592c1a42 completed March 10, 2026, 12:32 a.m.
NED2 Entity disambiguation (via description) batch_69af6760cf7c8190bb681f573828049e completed March 10, 2026, 12:35 a.m.
Created at: March 6, 2026, 9:48 p.m.