Triple

T7161885
Position Surface form Disambiguated ID Type / Status
Subject Central Bantu E166965 entity
Predicate hasMajorLanguage P207 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: [Central Bantu, hasMajorLanguage, Lunda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lunda
Context triple: [Central Bantu, hasMajorLanguage, 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_69c68887a5cc8190bec0ea96227164f7 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e82e4b248190ad3c3863cb93971e completed March 27, 2026, 8:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7b8fae30481909c39d68fb828a2c0 completed March 28, 2026, 11:18 a.m.
Created at: March 27, 2026, 2:47 p.m.