Triple

T6771173
Position Surface form Disambiguated ID Type / Status
Subject Fon language E155044 entity
Predicate closelyRelatedTo P37 FINISHED
Object Aja language E267651 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: Aja language | Statement: [Fon language, closelyRelatedTo, Aja language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aja language
Context triple: [Fon language, closelyRelatedTo, Aja language]
  • A. Aja language chosen
    The Aja language is a Gbe language of the Niger-Congo family spoken primarily in parts of Benin and Togo.
  • B. Ngaju language
    The Ngaju language is an Austronesian language spoken primarily by the Ngaju Dayak people of central Kalimantan in Indonesian Borneo.
  • C. Awajún language
    Awajún language is an indigenous Jivaroan language spoken primarily by the Awajún (Aguaruna) people of northern Peru.
  • D. Akawaio language
    The Akawaio language is an indigenous Cariban language spoken by the Akawaio people of Guyana, Venezuela, and Brazil.
  • E. Sanglechi language
    The Sanglechi language is an Eastern Iranian language spoken by a small community in the Sanglech Valley region of Afghanistan and Tajikistan.
  • 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_69c68812ef7c819099369f51febb725c completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d2496fa08190895d8b625fb0d699 completed March 27, 2026, 6:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69c712c46b70819097401afab991c808 completed March 27, 2026, 11:29 p.m.
Created at: March 27, 2026, 2:13 p.m.