Triple

T721229
Position Surface form Disambiguated ID Type / Status
Subject Fang language E14619 entity
Predicate hasDialect P4251 FINISHED
Object Ntumu
Ntumu is a dialect of the Fang language spoken by Fang communities in parts of Central Africa, particularly in Equatorial Guinea, Gabon, and Cameroon.
 E95328 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: Ntumu | Statement: [Fang language, hasDialect, Ntumu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ntumu
Context triple: [Fang language, hasDialect, Ntumu]
  • 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. Murambi
    Murambi is a residential suburb of Mutare, a major city in eastern Zimbabwe.
  • C. Sakubva
    Sakubva is a high-density residential suburb of Mutare in eastern Zimbabwe, known as one of the city’s oldest and most populous townships.
  • D. Mwanza
    Mwanza is a major port city in northwestern Tanzania, situated on the southern shores of Lake Victoria and serving as a key commercial and transport hub for the region.
  • E. Ngozi
    Ngozi is a Nigerian given name of Igbo origin commonly used for females and meaning "blessing."
  • 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: Ntumu
Triple: [Fang language, hasDialect, Ntumu]
Generated description
Ntumu is a dialect of the Fang language spoken by Fang communities in parts of Central Africa, particularly in Equatorial Guinea, Gabon, and Cameroon.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ntumu
Target entity description: Ntumu is a dialect of the Fang language spoken by Fang communities in parts of Central Africa, particularly in Equatorial Guinea, Gabon, and Cameroon.
  • 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. Murambi
    Murambi is a residential suburb of Mutare, a major city in eastern Zimbabwe.
  • C. Sakubva
    Sakubva is a high-density residential suburb of Mutare in eastern Zimbabwe, known as one of the city’s oldest and most populous townships.
  • D. Mwanza
    Mwanza is a major port city in northwestern Tanzania, situated on the southern shores of Lake Victoria and serving as a key commercial and transport hub for the region.
  • E. Ngozi
    Ngozi is a Nigerian given name of Igbo origin commonly used for females and meaning "blessing."
  • 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_69a4934c753c81909b309027e48b9b3a completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a58fa41c819082de2cc4e0cb2943 completed March 1, 2026, 8:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69a6891b6d908190823a9a09cef5c7da completed March 3, 2026, 7:09 a.m.
NEDg Description generation batch_69a68b72910081908792bc4760b0610a completed March 3, 2026, 7:19 a.m.
NED2 Entity disambiguation (via description) batch_69a6d634d6f08190a59ebda3bfe0d3db completed March 3, 2026, 12:38 p.m.
Created at: March 1, 2026, 7:37 p.m.