Triple

T16567873
Position Surface form Disambiguated ID Type / Status
Subject Mandara language E402508 entity
Predicate hasDialect P4251 FINISHED
Object Malgwa dialect
The Malgwa dialect is a regional variety of the Mandara language spoken by communities in parts of northern Cameroon and neighboring areas.
E1221465 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: Malgwa dialect | Statement: [Mandara language, hasDialect, Malgwa dialect]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Malgwa dialect
Context triple: [Mandara language, hasDialect, Malgwa dialect]
  • A. Malwai dialect
    The Malwai dialect is a regional variety of Punjabi primarily spoken in the Malwa region of the Indian state of Punjab.
  • B. Malgavet dialect
    The Malgavet dialect is a regional variety of the Lihir language spoken on the Lihir Islands of Papua New Guinea.
  • C. Mulgi dialect
    The Mulgi dialect is a traditional regional variety of South Estonian spoken historically in the Mulgimaa area of southern Estonia.
  • D. Nankani dialect
    The Nankani dialect is a regional variety of the Gur-language cluster spoken in northern Ghana and southern Burkina Faso, associated with the Nankani people.
  • E. Werchikwar dialect
    The Werchikwar dialect is a regional variety of the Burushaski language spoken by Burusho communities in parts of northern Pakistan.
  • 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: Malgwa dialect
Triple: [Mandara language, hasDialect, Malgwa dialect]
Generated description
The Malgwa dialect is a regional variety of the Mandara language spoken by communities in parts of northern Cameroon and neighboring areas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Malgwa dialect
Target entity description: The Malgwa dialect is a regional variety of the Mandara language spoken by communities in parts of northern Cameroon and neighboring areas.
  • A. Malwai dialect
    The Malwai dialect is a regional variety of Punjabi primarily spoken in the Malwa region of the Indian state of Punjab.
  • B. Malgavet dialect
    The Malgavet dialect is a regional variety of the Lihir language spoken on the Lihir Islands of Papua New Guinea.
  • C. Mulgi dialect
    The Mulgi dialect is a traditional regional variety of South Estonian spoken historically in the Mulgimaa area of southern Estonia.
  • D. Nankani dialect
    The Nankani dialect is a regional variety of the Gur-language cluster spoken in northern Ghana and southern Burkina Faso, associated with the Nankani people.
  • E. Werchikwar dialect
    The Werchikwar dialect is a regional variety of the Burushaski language spoken by Burusho communities in parts of northern Pakistan.
  • 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_69d8838648088190acf97ef11fc3f61b completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e35772f6608190a125c7d3c199c3e2 completed April 18, 2026, 10:05 a.m.
NED1 Entity disambiguation (via context triple) batch_6a006ee3dcbc819087ea66b262585232 completed May 10, 2026, 11:41 a.m.
NEDg Description generation batch_6a006ff5bdb88190be90d7446e24b61f completed May 10, 2026, 11:45 a.m.
NED2 Entity disambiguation (via description) batch_6a007088fd988190b3dfef081769d03e completed May 10, 2026, 11:48 a.m.
Created at: April 10, 2026, 5:16 a.m.