Triple

T6974947
Position Surface form Disambiguated ID Type / Status
Subject Tanzania–Zambia Railway E161693 entity
Predicate terminus P388 FINISHED
Object Kapiri Mposhi
Kapiri Mposhi is a town in central Zambia that serves as a key rail and road junction linking the country to Tanzania and other regions.
 E637652 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: Kapiri Mposhi | Statement: [Tanzania–Zambia Railway, terminus, Kapiri Mposhi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kapiri Mposhi
Context triple: [Tanzania–Zambia Railway, terminus, Kapiri Mposhi]
  • A. Marondera
    Marondera is a town in eastern Zimbabwe known as an agricultural and educational center within the Mashonaland region.
  • B. Manzini
    Manzini is a major city in Eswatini that serves as an important commercial and transport hub of the country.
  • C. Kasane
    Kasane is a small town in northern Botswana that serves as a key gateway and service hub for visitors to Chobe National Park and the surrounding wildlife areas.
  • D. Hoedspruit
    Hoedspruit is a small South African town near Kruger National Park, known as a gateway to wildlife reserves and scenic Lowveld attractions.
  • E. Thohoyandou
    Thohoyandou is a town in South Africa’s Limpopo province that serves as an administrative, commercial, and educational hub for the surrounding region.
  • 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: Kapiri Mposhi
Triple: [Tanzania–Zambia Railway, terminus, Kapiri Mposhi]
Generated description
Kapiri Mposhi is a town in central Zambia that serves as a key rail and road junction linking the country to Tanzania and other regions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kapiri Mposhi
Target entity description: Kapiri Mposhi is a town in central Zambia that serves as a key rail and road junction linking the country to Tanzania and other regions.
  • A. Marondera
    Marondera is a town in eastern Zimbabwe known as an agricultural and educational center within the Mashonaland region.
  • B. Manzini
    Manzini is a major city in Eswatini that serves as an important commercial and transport hub of the country.
  • C. Kasane
    Kasane is a small town in northern Botswana that serves as a key gateway and service hub for visitors to Chobe National Park and the surrounding wildlife areas.
  • D. Hoedspruit
    Hoedspruit is a small South African town near Kruger National Park, known as a gateway to wildlife reserves and scenic Lowveld attractions.
  • E. Thohoyandou
    Thohoyandou is a town in South Africa’s Limpopo province that serves as an administrative, commercial, and educational hub for the surrounding region.
  • 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_69c68854a0d88190bc0bf82263f1afce completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6db3bda908190a10a91dc8d043ef1 completed March 27, 2026, 7:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7754815c8819091d1291f7cdfec1d completed March 28, 2026, 6:29 a.m.
NEDg Description generation batch_69c7798513cc8190b20c1bfea81e7d3c completed March 28, 2026, 6:47 a.m.
NED2 Entity disambiguation (via description) batch_69c77a1422d48190a0eaf90572e30caf completed March 28, 2026, 6:49 a.m.
Created at: March 27, 2026, 2:31 p.m.