Triple

T1806238
Position Surface form Disambiguated ID Type / Status
Subject Nairobi River E40225 entity
Predicate flowsThrough P225 FINISHED
Object Kibera
Kibera is one of Africa’s largest informal settlements, located in Nairobi, Kenya, known for its dense population, poverty, and vibrant community life.
 E212907 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: Kibera | Statement: [Nairobi River, flowsThrough, Kibera]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kibera
Context triple: [Nairobi River, flowsThrough, Kibera]
  • A. Nairobi
    Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
  • B. Nairobi
    Nairobi is a fan-favorite character from the Spanish series "Money Heist," known for her sharp leadership, optimism, and expertise in overseeing the gang’s money-printing operations.
  • C. Kadoma
    Kadoma is a city in Osaka Prefecture, Japan, known as a residential and commercial suburb within the Osaka metropolitan area.
  • D. Dandora
    Dandora is a residential and industrial area in Nairobi, Kenya, best known for hosting one of Africa’s largest open-air garbage dumps.
  • E. Mbare
    Mbare is one of the oldest and most densely populated townships in Harare, Zimbabwe, known as a major transport hub and bustling market area.
  • 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: Kibera
Triple: [Nairobi River, flowsThrough, Kibera]
Generated description
Kibera is one of Africa’s largest informal settlements, located in Nairobi, Kenya, known for its dense population, poverty, and vibrant community life.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kibera
Target entity description: Kibera is one of Africa’s largest informal settlements, located in Nairobi, Kenya, known for its dense population, poverty, and vibrant community life.
  • A. Nairobi
    Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
  • B. Nairobi
    Nairobi is a fan-favorite character from the Spanish series "Money Heist," known for her sharp leadership, optimism, and expertise in overseeing the gang’s money-printing operations.
  • C. Kadoma
    Kadoma is a city in Osaka Prefecture, Japan, known as a residential and commercial suburb within the Osaka metropolitan area.
  • D. Dandora
    Dandora is a residential and industrial area in Nairobi, Kenya, best known for hosting one of Africa’s largest open-air garbage dumps.
  • E. Mbare
    Mbare is one of the oldest and most densely populated townships in Harare, Zimbabwe, known as a major transport hub and bustling market area.
  • 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_69a88643a3388190a612f2ebe1fb29e7 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa659798b88190bd3070349ce6bebb completed March 6, 2026, 5:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69adead0fb988190b403f5c62cbe991a completed March 8, 2026, 9:32 p.m.
NEDg Description generation batch_69adeb5118608190be99a12b7f6b97c5 completed March 8, 2026, 9:34 p.m.
NED2 Entity disambiguation (via description) batch_69adec25a87081908f098df81de6eafb completed March 8, 2026, 9:37 p.m.
Created at: March 4, 2026, 7:32 p.m.