Triple

T21665238
Position Surface form Disambiguated ID Type / Status
Subject Makonde E534696 entity
Predicate closelyRelatedTo P37 FINISHED
Object Mwera language
The Mwera language is a Bantu language spoken primarily in southeastern Tanzania by the Mwera people.
 E1495218 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: Mwera language | Statement: [Makonde, closelyRelatedTo, Mwera language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mwera language
Context triple: [Makonde, closelyRelatedTo, Mwera language]
  • A. Nyungwe language
    Nyungwe is a Bantu language of the Sena–Nyanja group spoken primarily in central Mozambique.
  • B. Nyamwezi language
    The Nyamwezi language is a Bantu language spoken primarily by the Nyamwezi people of western-central Tanzania.
  • C. Lusoga language
    The Lusoga language is a Bantu language spoken primarily by the Basoga people in eastern Uganda.
  • D. 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.
  • E. Mwaghavul language
    The Mwaghavul language is a West Chadic language spoken primarily in Plateau State, central Nigeria, by the Mwaghavul people.
  • 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: Mwera language
Triple: [Makonde, closelyRelatedTo, Mwera language]
Generated description
The Mwera language is a Bantu language spoken primarily in southeastern Tanzania by the Mwera people.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mwera language
Target entity description: The Mwera language is a Bantu language spoken primarily in southeastern Tanzania by the Mwera people.
  • A. Nyungwe language
    Nyungwe is a Bantu language of the Sena–Nyanja group spoken primarily in central Mozambique.
  • B. Nyamwezi language
    The Nyamwezi language is a Bantu language spoken primarily by the Nyamwezi people of western-central Tanzania.
  • C. Lusoga language
    The Lusoga language is a Bantu language spoken primarily by the Basoga people in eastern Uganda.
  • D. 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.
  • E. Mwaghavul language
    The Mwaghavul language is a West Chadic language spoken primarily in Plateau State, central Nigeria, by the Mwaghavul people.
  • 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_69e0c467e1f48190af2650b19175abc4 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef6c0b26c8819092c13e59dcc3c25c completed April 27, 2026, 2 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a15a0fcb8819095a7ada7e746a1ef completed May 17, 2026, 7:23 p.m.
NEDg Description generation batch_6a0a165552f48190aab079b317c6e281 completed May 17, 2026, 7:26 p.m.
NED2 Entity disambiguation (via description) batch_6a0a1704fb4c819086a689ccced74f90 completed May 17, 2026, 7:29 p.m.
Created at: April 16, 2026, 6:36 p.m.