Triple

T15944853
Position Surface form Disambiguated ID Type / Status
Subject Gamo E386657 entity
Predicate neighboringLanguages P16383 FINISHED
Object Gofa E386658 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: Gofa | Statement: [Gamo, neighboringLanguages, Gofa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gofa
Context triple: [Gamo, neighboringLanguages, Gofa]
  • A. Gofa chosen
    Gofa is an Omotic language spoken primarily by the Gofa people in southwestern Ethiopia.
  • B. Silla
    Silla was an ancient Korean kingdom that unified most of the Korean Peninsula in the 7th century and played a central role in the development of early Korean culture, Buddhism, and statehood.
  • C. Gabbeh
    Gabbeh is a 1996 Iranian art film by Mohsen Makhmalbaf, celebrated for its poetic storytelling and richly colored, visually striking depiction of rural nomadic life.
  • D. Gizo
    Gizo is a small island town in the Solomon Islands known as an administrative and commercial hub in the western part of the country and a popular base for diving and marine tourism.
  • E. Sofy
    Sofy is a software testing platform that provides no-code, AI-driven tools for automated mobile and web app testing.
  • 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_69d86da882448190a82ea962fe343b79 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e156d0d55c8190af59ff169e8add78 completed April 16, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffb5beabbc8190977f14c1b3ccdf29 completed May 9, 2026, 10:31 p.m.
Created at: April 10, 2026, 4:53 a.m.