Triple

T14158134
Position Surface form Disambiguated ID Type / Status
Subject Macua E350865 entity
Predicate relatedLanguage P10003 FINISHED
Object Makonde language
The Makonde language is a Bantu language spoken primarily by the Makonde people of northern Mozambique and southern Tanzania.
 E1083821 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: Makonde language | Statement: [Macua, relatedLanguage, Makonde language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Makonde language
Context triple: [Macua, relatedLanguage, Makonde language]
  • A. Kaonde language
    The Kaonde language is a Bantu language spoken primarily by the Kaonde people of northwestern Zambia and parts of the Democratic Republic of the Congo.
  • B. Ngindo language
    The Ngindo language is a Bantu language spoken by the Ngindo people of southeastern Tanzania.
  • C. Mbunda language
    The Mbunda language is a Bantu language spoken primarily by the Mbunda people in parts of Angola and Zambia.
  • D. Konongo language
    The Konongo language is a Bantu language of East Africa, closely related to Sukuma and spoken by the Konongo people.
  • E. Nsenga language
    The Nsenga language is a Bantu language spoken primarily in Zambia and neighboring regions, closely related to other languages of the area such as Tumbuka and Chewa.
  • 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: Makonde language
Triple: [Macua, relatedLanguage, Makonde language]
Generated description
The Makonde language is a Bantu language spoken primarily by the Makonde people of northern Mozambique and southern Tanzania.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Makonde language
Target entity description: The Makonde language is a Bantu language spoken primarily by the Makonde people of northern Mozambique and southern Tanzania.
  • A. Kaonde language
    The Kaonde language is a Bantu language spoken primarily by the Kaonde people of northwestern Zambia and parts of the Democratic Republic of the Congo.
  • B. Ngindo language
    The Ngindo language is a Bantu language spoken by the Ngindo people of southeastern Tanzania.
  • C. Mbunda language
    The Mbunda language is a Bantu language spoken primarily by the Mbunda people in parts of Angola and Zambia.
  • D. Konongo language
    The Konongo language is a Bantu language of East Africa, closely related to Sukuma and spoken by the Konongo people.
  • E. Nsenga language
    The Nsenga language is a Bantu language spoken primarily in Zambia and neighboring regions, closely related to other languages of the area such as Tumbuka and Chewa.
  • 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_69d8278775fc8190b0802d22ca2f495d completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de61377de48190a3470d28f0edd34a completed April 14, 2026, 3:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcf7ef4d80819098d210503f5d22e9 completed May 7, 2026, 8:37 p.m.
NEDg Description generation batch_69fd02cee5e0819086718893d1621481 completed May 7, 2026, 9:23 p.m.
NED2 Entity disambiguation (via description) batch_69fd063668f4819099d52bee7e7cdc32 completed May 7, 2026, 9:37 p.m.
Created at: April 10, 2026, 12:58 a.m.