Triple

T16383692
Position Surface form Disambiguated ID Type / Status
Subject Benga language E397868 entity
Predicate hasAlternativeName P39 FINISHED
Object Benga E1210226 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: Benga | Statement: [Benga language, hasAlternativeName, Benga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Benga
Context triple: [Benga language, hasAlternativeName, Benga]
  • A. Benga chosen
    Benga is a Bantu language spoken primarily by the Benga people along the coastal regions and islands of Equatorial Guinea and northern Gabon.
  • B. Dor Bongo
    Dor Bongo is an alternative name for the Bongo language, a Central Sudanic language spoken primarily in South Sudan.
  • C. Mengoni
    Mengoni is an Italian surname most notably associated with figures such as architect Giuseppe Mengoni and contemporary singer Marco Mengoni.
  • D. Bangala
    Bangala is a regional variety of the Bantu language Lingala, spoken primarily in parts of the Democratic Republic of the Congo and neighboring areas.
  • E. Bongo
    Bongo is an animated musical segment from Disney’s 1947 anthology film "Fun and Fancy Free," following the adventures of a circus bear who longs for freedom and love.
  • 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_69d87f2880b48190ae1a9673a3bbef80 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e319de83248190b5d43646fa9b6cda completed April 18, 2026, 5:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a003c55bba481909bca6cc17e1dfcf5 completed May 10, 2026, 8:05 a.m.
Created at: April 10, 2026, 5:08 a.m.