Triple

T7954671
Position Surface form Disambiguated ID Type / Status
Subject Bantu H languages E184701 entity
Predicate hasNotableLanguage P7390 FINISHED
Object Lunda E336257 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: Lunda | Statement: [Bantu H languages, hasNotableLanguage, Lunda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lunda
Context triple: [Bantu H languages, hasNotableLanguage, Lunda]
  • A. Lunda chosen
    Lunda is a Bantu language spoken primarily by the Lunda people in parts of Zambia, Angola, and the Democratic Republic of the Congo.
  • B. Luba
    Luba is a coastal town and important port on the southern part of Bioko Island in Equatorial Guinea.
  • C. Luba
    The Luba are a major Bantu-speaking ethnic group of Central Africa, historically known for the powerful Luba Kingdom centered in what is now the Democratic Republic of the Congo.
  • D. Nakonde
    Nakonde is a town in northeastern Zambia near the border with Tanzania, serving as a key border crossing and trade hub between the two countries.
  • E. Soshanguve
    Soshanguve is a large township in the northern part of the Gauteng province of South Africa, known for its diverse population and proximity to Pretoria.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca8292cba881908a64427b938dac47 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3b5f74308190aa63a22c3d5feacc completed March 31, 2026, 3:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccbdf7679881909a14c4786c8e3e76 completed April 1, 2026, 6:40 a.m.
Created at: March 30, 2026, 5:11 p.m.