Triple

T10910208
Position Surface form Disambiguated ID Type / Status
Subject Simba Makoni E257676 entity
Predicate familyName P18 FINISHED
Object Makoni
Makoni is a Zimbabwean surname most prominently associated with Simba Makoni, a politician and former finance minister of Zimbabwe.
 E328808 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: Makoni | Statement: [Simba Makoni, familyName, Makoni]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Makoni
Context triple: [Simba Makoni, familyName, Makoni]
  • A. Makoni
    Makoni is a town in Zimbabwe’s Manicaland Province, known primarily as a local administrative and commercial center for the surrounding rural district.
  • B. Mberengwa
    Mberengwa is a rural district and growth point in Zimbabwe known for its mining activities and location in the southern part of the Midlands Province.
  • C. Thabazimbi
    Thabazimbi is a small mining town in South Africa’s Limpopo province, known for its iron ore industry and proximity to the scenic Marakele National Park.
  • D. Mzuzu
    Mzuzu is a major city in northern Malawi known as an important commercial and administrative center for the region.
  • E. Nacala
    Nacala is a coastal city in northern Mozambique known for its deep-water natural harbor and role as a major regional port and transport hub.
  • 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: Makoni
Triple: [Simba Makoni, familyName, Makoni]
Generated description
Makoni is a Zimbabwean surname most prominently associated with Simba Makoni, a politician and former finance minister of Zimbabwe.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Makoni
Target entity description: Makoni is a Zimbabwean surname most prominently associated with Simba Makoni, a politician and former finance minister of Zimbabwe.
  • A. Makoni chosen
    Makoni is a town in Zimbabwe’s Manicaland Province, known primarily as a local administrative and commercial center for the surrounding rural district.
  • B. Mberengwa
    Mberengwa is a rural district and growth point in Zimbabwe known for its mining activities and location in the southern part of the Midlands Province.
  • C. Thabazimbi
    Thabazimbi is a small mining town in South Africa’s Limpopo province, known for its iron ore industry and proximity to the scenic Marakele National Park.
  • D. Mzuzu
    Mzuzu is a major city in northern Malawi known as an important commercial and administrative center for the region.
  • E. Nacala
    Nacala is a coastal city in northern Mozambique known for its deep-water natural harbor and role as a major regional port and transport hub.
  • F. None of above.

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_69d6aa864ed88190818280ab6791d065 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d77068e5488190bbc881ebf51d6b2e completed April 9, 2026, 9:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69e2d6ec38408190929b5e60a9a3e81e completed April 18, 2026, 12:57 a.m.
NEDg Description generation batch_69e2ff1ddd2c8190b31f5007f7492a4e completed April 18, 2026, 3:48 a.m.
NED2 Entity disambiguation (via description) batch_69e3260494bc81909e3dd4829697fb72 completed April 18, 2026, 6:34 a.m.
Created at: April 8, 2026, 9:22 p.m.