Triple

T15553235
Position Surface form Disambiguated ID Type / Status
Subject Mokgweetsi Masisi E370804 entity
Predicate givenName P17 FINISHED
Object Keabetswe
Keabetswe is the given first name of Mokgweetsi Masisi, the president of Botswana.
 E1164446 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: Keabetswe | Statement: [Mokgweetsi Masisi, givenName, Keabetswe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Keabetswe
Context triple: [Mokgweetsi Masisi, givenName, Keabetswe]
  • A. Kagiso
    Kagiso is a township in South Africa’s Gauteng province, situated west of Johannesburg and known for its dense residential communities and vibrant local culture.
  • B. Tshela
    Tshela is a town in the western Democratic Republic of the Congo, situated in the forested interior of Kongo Central Province near the border with the Republic of the Congo.
  • C. Tembisa
    Tembisa is a large township in Gauteng, South Africa, situated on the East Rand and known as a densely populated residential area within the City of Ekurhuleni.
  • D. Bophelong
    Bophelong is a township in the Emfuleni area of Gauteng, South Africa, known as a residential community near the industrial city of Vanderbijlpark.
  • E. Gavinana
    Gavinana is a residential district in the southeastern part of Florence, Italy, known for its modern urban layout and proximity to the Arno River.
  • 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: Keabetswe
Triple: [Mokgweetsi Masisi, givenName, Keabetswe]
Generated description
Keabetswe is the given first name of Mokgweetsi Masisi, the president of Botswana.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Keabetswe
Target entity description: Keabetswe is the given first name of Mokgweetsi Masisi, the president of Botswana.
  • A. Kagiso
    Kagiso is a township in South Africa’s Gauteng province, situated west of Johannesburg and known for its dense residential communities and vibrant local culture.
  • B. Tshela
    Tshela is a town in the western Democratic Republic of the Congo, situated in the forested interior of Kongo Central Province near the border with the Republic of the Congo.
  • C. Tembisa
    Tembisa is a large township in Gauteng, South Africa, situated on the East Rand and known as a densely populated residential area within the City of Ekurhuleni.
  • D. Bophelong
    Bophelong is a township in the Emfuleni area of Gauteng, South Africa, known as a residential community near the industrial city of Vanderbijlpark.
  • E. Gavinana
    Gavinana is a residential district in the southeastern part of Florence, Italy, known for its modern urban layout and proximity to the Arno River.
  • 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_69d85cc6cf40819091f4a5facee1ebe6 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04a96c0c88190808f68601a36b506 completed April 16, 2026, 2:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff4c3e67c881909a9fa1e483a364be completed May 9, 2026, 3:01 p.m.
NEDg Description generation batch_69ff4d7e60dc8190aa80cb269b1811bc completed May 9, 2026, 3:06 p.m.
NED2 Entity disambiguation (via description) batch_69ff4e03e8748190a23e7577accaf04a completed May 9, 2026, 3:08 p.m.
Created at: April 10, 2026, 4:09 a.m.