Triple

T19145334
Position Surface form Disambiguated ID Type / Status
Subject Gbaya E468663 entity
Predicate language P15 FINISHED
Object Toongo language NE NERFINISHED

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: Toongo language | Statement: [Gbaya, language, Toongo language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Toongo language
Context triple: [Gbaya, language, Toongo language]
  • A. Toongo language chosen
    The Toongo language is a lesser-known Gbaya language spoken by a small ethnic community in Central Africa.
  • B. Touo language
    Touo is an Oceanic language spoken by a small community in the Solomon Islands, noted for its distinct phonology and limited number of speakers.
  • C. Tonsea language
    Tonsea is an Austronesian language spoken by the Tonsea people of North Sulawesi, Indonesia, and is one of the traditional Minahasan languages of the region.
  • D. Toundanow language
    The Toundanow language is an Austronesian language spoken by the Tonsawang people of northern Sulawesi, Indonesia.
  • E. Tindi language
    The Tindi language is a Northeast Caucasian language spoken by the Tindi people in Dagestan, Russia, known for its complex grammar and limited number of speakers.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8dd084ff48190ac0f8c46ee722629 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e978b0b481909a531efa030c5def completed April 20, 2026, 8:53 a.m.
Created at: April 10, 2026, 12:06 p.m.