Triple

T3198819
Position Surface form Disambiguated ID Type / Status
Subject Cuando River E66995 entity
Predicate hasAlternativeName P39 FINISHED
Object Kwando
Kwando is a river in southern Africa that flows through Angola, Namibia, and Botswana, forming part of the region’s complex wetland and river system.
 E336259 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: Kwando | Statement: [Cuando River, hasAlternativeName, Kwando]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kwando
Context triple: [Cuando River, hasAlternativeName, Kwando]
  • A. Nyanda
    Nyanda is the former name of Masvingo, a historic city in southeastern Zimbabwe known for its proximity to the Great Zimbabwe ruins.
  • B. Kikamba-Doondo
    Kikamba-Doondo is a regional dialect of the Bantu language Kikongo, spoken by communities in parts of Central Africa.
  • C. Ngamo
    Ngamo is a West Chadic language spoken primarily in northeastern Nigeria by the Ngamo people.
  • D. Mukuzani
    Mukuzani is a renowned Georgian red wine appellation known for producing dry, oak-aged wines from the Saperavi grape in the Kakheti region.
  • E. Datooga
    Datooga is a Southern Nilotic language spoken primarily by the Datooga people of north-central Tanzania.
  • 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: Kwando
Triple: [Cuando River, hasAlternativeName, Kwando]
Generated description
Kwando is a river in southern Africa that flows through Angola, Namibia, and Botswana, forming part of the region’s complex wetland and river system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kwando
Target entity description: Kwando is a river in southern Africa that flows through Angola, Namibia, and Botswana, forming part of the region’s complex wetland and river system.
  • A. Nyanda
    Nyanda is the former name of Masvingo, a historic city in southeastern Zimbabwe known for its proximity to the Great Zimbabwe ruins.
  • B. Kikamba-Doondo
    Kikamba-Doondo is a regional dialect of the Bantu language Kikongo, spoken by communities in parts of Central Africa.
  • C. Ngamo
    Ngamo is a West Chadic language spoken primarily in northeastern Nigeria by the Ngamo people.
  • D. Mukuzani
    Mukuzani is a renowned Georgian red wine appellation known for producing dry, oak-aged wines from the Saperavi grape in the Kakheti region.
  • E. Datooga
    Datooga is a Southern Nilotic language spoken primarily by the Datooga people of north-central Tanzania.
  • 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_69ad8589bd988190afa7ed2bdffb7b33 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada9abe8f88190a4c2ccc31add7959 completed March 8, 2026, 4:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b24bbdc9908190b5d8328f6fbc6002 completed March 12, 2026, 5:14 a.m.
NEDg Description generation batch_69b25034bc4481909c860099cc1aa311 completed March 12, 2026, 5:33 a.m.
NED2 Entity disambiguation (via description) batch_69b251458ab48190a374f910103763f2 completed March 12, 2026, 5:38 a.m.
Created at: March 8, 2026, 3:07 p.m.