Triple

T15892676
Position Surface form Disambiguated ID Type / Status
Subject Lunda people E385366 entity
Predicate language P15 FINISHED
Object Lunda language E720318 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 language | Statement: [Lunda people, language, Lunda language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lunda language
Context triple: [Lunda people, language, Lunda language]
  • A. Lunda language chosen
    The Lunda language is a Bantu language spoken primarily in parts of Angola, the Democratic Republic of the Congo, and Zambia by the Lunda people.
  • B. Longuda language
    Longuda language is a Niger-Congo language spoken by the Longuda people of northeastern Nigeria.
  • C. Mbunda language
    The Mbunda language is a Bantu language spoken primarily by the Mbunda people in parts of Angola and Zambia.
  • D. Azande language
    Azande language is a Ubangian language spoken primarily by the Azande people across parts of South Sudan, the Central African Republic, and the Democratic Republic of the Congo.
  • E. Banda-Ndélé language
    The Banda-Ndélé language is a Central Sudanic language spoken by the Banda people, primarily in the Central African Republic.
  • 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_69d86da5b800819083a31be937d738b0 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1563727cc819086b5c18b655dd7f6 completed April 16, 2026, 9:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffb0497cb481908e8ea4ebb9c4039d completed May 9, 2026, 10:08 p.m.
Created at: April 10, 2026, 4:51 a.m.