Triple

T12225873
Position Surface form Disambiguated ID Type / Status
Subject Ekpeye E291350 entity
Predicate language P15 FINISHED
Object Ekpeye language
Ekpeye language is a Niger-Congo language spoken by the Ekpeye people of Rivers State in southern Nigeria.
E972785 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: Ekpeye language | Statement: [Ekpeye, language, Ekpeye language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ekpeye language
Context triple: [Ekpeye, language, Ekpeye language]
  • A. Teke-Kukuya language
    The Teke-Kukuya language is a Bantu language spoken by the Teke-Kukuya people in the Republic of the Congo and neighboring regions of Central Africa.
  • B. Nupe language
    The Nupe language is a Niger-Congo language spoken primarily by the Nupe people in central Nigeria, especially in the Middle Belt region.
  • C. Ikwerre language
    Ikwerre language is an Igboid language spoken primarily by the Ikwerre people in Rivers State, Nigeria.
  • D. Keapara language
    The Keapara language is an Austronesian language of coastal Papua New Guinea spoken by the Keapara people in Central Province.
  • E. Teke-Kega language
    The Teke-Kega language is a Bantu language spoken by the Teke people of Central Africa, primarily in the Republic of the Congo and surrounding regions.
  • 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: Ekpeye language
Triple: [Ekpeye, language, Ekpeye language]
Generated description
Ekpeye language is a Niger-Congo language spoken by the Ekpeye people of Rivers State in southern Nigeria.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Ekpeye language
Target entity description: Ekpeye language is a Niger-Congo language spoken by the Ekpeye people of Rivers State in southern Nigeria.
  • A. Teke-Kukuya language
    The Teke-Kukuya language is a Bantu language spoken by the Teke-Kukuya people in the Republic of the Congo and neighboring regions of Central Africa.
  • B. Nupe language
    The Nupe language is a Niger-Congo language spoken primarily by the Nupe people in central Nigeria, especially in the Middle Belt region.
  • C. Ikwerre language
    Ikwerre language is an Igboid language spoken primarily by the Ikwerre people in Rivers State, Nigeria.
  • D. Keapara language
    The Keapara language is an Austronesian language of coastal Papua New Guinea spoken by the Keapara people in Central Province.
  • E. Teke-Kega language
    The Teke-Kega language is a Bantu language spoken by the Teke people of Central Africa, primarily in the Republic of the Congo and surrounding regions.
  • 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_69d6ab668acc8190963ba424049d6aee completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d91ca2101c8190955c36704935036a completed April 10, 2026, 3:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f60aa9023881909f8373e02d2cad4b completed May 2, 2026, 2:31 p.m.
NEDg Description generation batch_69f61a13fd1481908a06ca65b276e0e1 completed May 2, 2026, 3:36 p.m.
NED2 Entity disambiguation (via description) batch_69f61ad2bd0c8190ada37bc1f8ae160f completed May 2, 2026, 3:40 p.m.
Created at: April 8, 2026, 9:51 p.m.