Triple

T14069912
Position Surface form Disambiguated ID Type / Status
Subject Fataleka people E338577 entity
Predicate language P15 FINISHED
Object Fataleka language E157637 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: Fataleka language | Statement: [Fataleka people, language, Fataleka language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fataleka language
Context triple: [Fataleka people, language, Fataleka language]
  • A. Fataleka language chosen
    The Fataleka language is an Austronesian language spoken by the Fataleka people on Malaita in the Solomon Islands.
  • B. Malasanga language
    The Malasanga language is an Oceanic language spoken in Papua New Guinea, belonging to the Kula–Malasanga subgroup of the Austronesian language family.
  • C. Baliledu language
    The Baliledu language is an Austronesian language of the Bima–Sumba subgroup spoken by a local community in eastern Indonesia.
  • D. Kalanga language
    The Kalanga language is a Bantu language spoken primarily by the Kalanga people in parts of Botswana and southwestern Zimbabwe.
  • E. Bafia language
    The Bafia language is a Bantu language spoken primarily by the Bafia people in central Cameroon.
  • 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_69d81c67ba6c819091935650dfb3b895 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de568d0404819087e0fe37c72162cb completed April 14, 2026, 3 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcb66cfe2c8190af8354316d4f4df9 completed May 7, 2026, 3:57 p.m.
Created at: April 9, 2026, 10:21 p.m.