Triple

T6366919
Position Surface form Disambiguated ID Type / Status
Subject Ovambo E143250 entity
Predicate hasDialect P4251 FINISHED
Object Eunda
Eunda is a dialect of the Ovambo (Oshiwambo) language spoken by the Ovambo people of northern Namibia and southern Angola.
 E589689 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: Eunda | Statement: [Ovambo, hasDialect, Eunda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Eunda
Context triple: [Ovambo, hasDialect, Eunda]
  • A. Bundi
    Bundi is a historic city in Rajasthan, India, renowned for its ornate palaces, stepwells, and well-preserved medieval architecture.
  • B. Urunga
    Urunga is a small coastal town in New South Wales, Australia, known for its scenic boardwalks, estuary views, and relaxed seaside atmosphere.
  • C. Yenda
    Yenda is a small rural town in the Riverina region of New South Wales, Australia, known for its agricultural production, particularly viticulture and horticulture.
  • D. Moura
    Moura is a small coal-mining town in Central Queensland, Australia, known for its agricultural activities and history of mining disasters.
  • E. Moura
    Moura is a historic town in Portugal’s Alentejo region, known for its whitewashed architecture, olive oil production, and proximity to the Alqueva reservoir.
  • 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: Eunda
Triple: [Ovambo, hasDialect, Eunda]
Generated description
Eunda is a dialect of the Ovambo (Oshiwambo) language spoken by the Ovambo people of northern Namibia and southern Angola.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Eunda
Target entity description: Eunda is a dialect of the Ovambo (Oshiwambo) language spoken by the Ovambo people of northern Namibia and southern Angola.
  • A. Bundi
    Bundi is a historic city in Rajasthan, India, renowned for its ornate palaces, stepwells, and well-preserved medieval architecture.
  • B. Urunga
    Urunga is a small coastal town in New South Wales, Australia, known for its scenic boardwalks, estuary views, and relaxed seaside atmosphere.
  • C. Yenda
    Yenda is a small rural town in the Riverina region of New South Wales, Australia, known for its agricultural production, particularly viticulture and horticulture.
  • D. Moura
    Moura is a small coal-mining town in Central Queensland, Australia, known for its agricultural activities and history of mining disasters.
  • E. Moura
    Moura is a historic town in Portugal’s Alentejo region, known for its whitewashed architecture, olive oil production, and proximity to the Alqueva reservoir.
  • 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_69c008d8c61081908bcaf61510d881ed completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c06811de2881909ead116117956981 completed March 22, 2026, 10:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6386f361c819098dbe01b0cb07b06 completed March 27, 2026, 7:57 a.m.
NEDg Description generation batch_69c6395e934c81909bfa10ee1b8e517f completed March 27, 2026, 8:01 a.m.
NED2 Entity disambiguation (via description) batch_69c63a0a4a108190b474555d8cb1540c completed March 27, 2026, 8:04 a.m.
Created at: March 22, 2026, 4:32 p.m.