Triple

T9671708
Position Surface form Disambiguated ID Type / Status
Subject Great Lakes Bantu languages E234044 entity
Predicate hasMember P10 FINISHED
Object Haya E725055 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: Haya | Statement: [Great Lakes Bantu languages, hasMember, Haya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Haya
Context triple: [Great Lakes Bantu languages, hasMember, Haya]
  • A. Haya
    Haya is a feminine given name of Arabic origin, commonly used in the Middle East and among Arabic-speaking communities.
  • B. Haya chosen
    The Haya are a Bantu-speaking ethnic group of northwestern Tanzania, known for their advanced precolonial ironworking and intensive banana-based agriculture around Lake Victoria.
  • C. Haruna
    Haruna was a Japanese Kongō-class fast battleship that served in the Imperial Japanese Navy during both World Wars and saw extensive action in the Pacific Theater.
  • D. Haisyn
    Haisyn is a city in central Ukraine known as a local administrative and economic center within Vinnytsia Oblast.
  • E. Hieda
    Hieda is a Japanese surname notably associated with historical and literary figures in classical Japanese records and folklore.
  • 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_69ca848f55e48190b3f67252571c3d45 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9c6949108190b699442e5c2aacf9 completed April 1, 2026, 10:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69d18a28d1c481908737992466d35fdd completed April 4, 2026, 10:01 p.m.
Created at: March 30, 2026, 8:15 p.m.