Triple

T2286358
Position Surface form Disambiguated ID Type / Status
Subject Tigray Region E51399 entity
Predicate officialLanguage P236 FINISHED
Object Tigrinya E41857 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: Tigrinya | Statement: [Tigray Region, officialLanguage, Tigrinya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tigrinya
Context triple: [Tigray Region, officialLanguage, Tigrinya]
  • A. Tigrinya chosen
    Tigrinya is a Semitic language spoken primarily in Eritrea and northern Ethiopia, serving as a major language of communication, education, and media in the region.
  • B. Amharic
    Amharic is a Semitic language widely spoken in Ethiopia and used as a major language of government, education, and media in the country.
  • C. Ge'ez
    Ge'ez is an ancient Semitic language of Ethiopia and Eritrea, best known today as the classical and liturgical language of the Ethiopian and Eritrean Orthodox Tewahedo Churches.
  • D. ትግርኛ
    ትግርኛ is a Semitic language primarily spoken in Eritrea and northern Ethiopia by the Tigrinya people.
  • E. Oromo
    Oromo is a Cushitic language widely spoken by the Oromo people, primarily in Ethiopia and parts of neighboring East African countries.
  • 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_69a88b09c644819090b503456d96bf70 completed March 4, 2026, 7:42 p.m.
NER Named-entity recognition batch_69abc24730208190af8a5cf443d334f7 completed March 7, 2026, 6:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae96080548819098ae6c5ab73036f2 completed March 9, 2026, 9:42 a.m.
Created at: March 4, 2026, 7:48 p.m.