Triple

T378223
Position Surface form Disambiguated ID Type / Status
Subject Harare E8616 entity
Predicate hasSuburb P747 FINISHED
Object 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.
 E48970 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: Mbare | Statement: [Harare, hasSuburb, Mbare]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mbare
Context triple: [Harare, hasSuburb, Mbare]
  • A. Mutare
    Mutare is a major city in eastern Zimbabwe, serving as the capital of Manicaland Province and an important commercial and transport hub near the border with Mozambique.
  • B. Nairobi
    Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
  • C. Bulawayo
    Bulawayo is Zimbabwe’s second-largest city and a major industrial, cultural, and transport hub in the southwestern part of the country.
  • D. Soweto
    Soweto is a historically significant township in Johannesburg, South Africa, known for its central role in the struggle against apartheid and its rich urban culture.
  • E. Masvingo
    Masvingo is one of Zimbabwe’s oldest urban centers, located in the country’s southeastern region near the Great Zimbabwe ruins.
  • 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: Mbare
Triple: [Harare, hasSuburb, Mbare]
Generated description
Mbare is one of the oldest and most densely populated townships in Harare, Zimbabwe, known as a major transport hub and bustling market area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mbare
Target entity description: Mbare is one of the oldest and most densely populated townships in Harare, Zimbabwe, known as a major transport hub and bustling market area.
  • A. Mutare
    Mutare is a major city in eastern Zimbabwe, serving as the capital of Manicaland Province and an important commercial and transport hub near the border with Mozambique.
  • B. Nairobi
    Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
  • C. Bulawayo
    Bulawayo is Zimbabwe’s second-largest city and a major industrial, cultural, and transport hub in the southwestern part of the country.
  • D. Soweto
    Soweto is a historically significant township in Johannesburg, South Africa, known for its central role in the struggle against apartheid and its rich urban culture.
  • E. Masvingo
    Masvingo is one of Zimbabwe’s oldest urban centers, located in the country’s southeastern region near the Great Zimbabwe ruins.
  • 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_69a2e7f47dd08190a4e294ccbbe46cd4 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ee2b0ec481908fac41a4e1d20468 completed Feb. 28, 2026, 1:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3fe92911881908ee7f88d5c628ec9 completed March 1, 2026, 8:53 a.m.
NEDg Description generation batch_69a3ff327d1c81909cbbbfd53fc74559 completed March 1, 2026, 8:56 a.m.
NED2 Entity disambiguation (via description) batch_69a40101f7e4819099bd776c79b4d8cc completed March 1, 2026, 9:04 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.