Triple

T6974963
Position Surface form Disambiguated ID Type / Status
Subject Tanzania–Zambia Railway E161693 entity
Predicate passesThrough P225 FINISHED
Object Mpika
Mpika is a town in northern Zambia that serves as an important regional transport hub and service center along the Tanzania–Zambia Railway.
 E633690 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: Mpika | Statement: [Tanzania–Zambia Railway, passesThrough, Mpika]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mpika
Context triple: [Tanzania–Zambia Railway, passesThrough, Mpika]
  • A. Mpanda
    Mpanda is a town in western Tanzania that serves as an important administrative and commercial hub for the surrounding region.
  • B. Kumba
    Kumba is a major town in southwestern Cameroon known as a commercial hub and cultural crossroads where languages like Cameroonian Pidgin English are widely used.
  • C. Kumba
    Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
  • D. Oshikwambi
    Oshikwambi is a regional dialect of the Oshiwambo language spoken by the Kwambi people in northern Namibia.
  • E. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • 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: Mpika
Triple: [Tanzania–Zambia Railway, passesThrough, Mpika]
Generated description
Mpika is a town in northern Zambia that serves as an important regional transport hub and service center along the Tanzania–Zambia Railway.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mpika
Target entity description: Mpika is a town in northern Zambia that serves as an important regional transport hub and service center along the Tanzania–Zambia Railway.
  • A. Mpanda
    Mpanda is a town in western Tanzania that serves as an important administrative and commercial hub for the surrounding region.
  • B. Kumba
    Kumba is a major town in southwestern Cameroon known as a commercial hub and cultural crossroads where languages like Cameroonian Pidgin English are widely used.
  • C. Kumba
    Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
  • D. Oshikwambi
    Oshikwambi is a regional dialect of the Oshiwambo language spoken by the Kwambi people in northern Namibia.
  • E. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • 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_69c761a6bbdc81908c96871f151db279 completed March 28, 2026, 5:05 a.m.
NEDg Description generation batch_69c7639074f48190bc095f18fc35c08b completed March 28, 2026, 5:13 a.m.
NED2 Entity disambiguation (via description) batch_69c76435fd2c819099143ea12f21d095 completed March 28, 2026, 5:16 a.m.
Created at: March 27, 2026, 2:31 p.m.