Triple

T23271008
Position Surface form Disambiguated ID Type / Status
Subject Ambo E588288 entity
Predicate speaks P741 FINISHED
Object Ovambo language NE NERFINISHED

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: Ovambo language | Statement: [Ambo, speaks, Ovambo language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ovambo language
Context triple: [Ambo, speaks, Ovambo language]
  • A. Ovambo language chosen
    The Ovambo language is a Bantu language spoken primarily in northern Namibia and southern Angola by the Ovambo people.
  • B. Kavango languages
    The Kavango languages are a group of closely related Bantu languages spoken primarily along the Kavango River region in Namibia and Angola.
  • C. Ngamo language
    The Ngamo language is a West Chadic language spoken primarily in northeastern Nigeria by the Ngamo people.
  • D. Haiǁom language
    The Haiǁom language is a Khoisan language spoken by the Haiǁom people of Namibia, known for its use of click consonants and close relation to Nama.
  • E. Teke-Ngungwel language
    The Teke-Ngungwel language is a Bantu language spoken by the Teke people in Central Africa, particularly in parts of the Republic of the Congo and neighboring regions.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e25d148adc819088efbf42672604e9 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f1957418fc819085ee528622e0c6de completed April 29, 2026, 5:21 a.m.
Created at: April 17, 2026, 4:45 p.m.