Triple

T36814316
Position Surface form Disambiguated ID Type / Status
Subject Nkomi E909683 entity
Predicate hasAlternativeName P39 FINISHED
Object Nkomi dialect
The Nkomi dialect is a regional variety of the Nkomi language spoken by the Nkomi people, characterized by its distinct phonological and lexical features within the broader Bantu language family.
E2201866 NE FINISHED

How this triple was built (2 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: Nkomi dialect | Statement: [Nkomi, hasAlternativeName, Nkomi dialect]
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: Nkomi dialect
Triple: [Nkomi, hasAlternativeName, Nkomi dialect]
Generated description
The Nkomi dialect is a regional variety of the Nkomi language spoken by the Nkomi people, characterized by its distinct phonological and lexical features within the broader Bantu language family.

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_69f76e7cbbf48190891227b14d041139 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f7ca70a7688190874a1931b90fa13a completed May 3, 2026, 10:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3dde5f21648190aa278d9c91a09f91 completed June 26, 2026, 2:05 a.m.
NEDg Description generation batch_6a3ddf82de40819096f8cd0c5e4f9fdc completed June 26, 2026, 2:10 a.m.
NED2 Entity disambiguation (via description) batch_6a3df4bf3db48190946180911b0494db completed June 26, 2026, 3:40 a.m.
Created at: May 3, 2026, 4:13 p.m.