Triple

T19157700
Position Surface form Disambiguated ID Type / Status
Subject ansuvimab E468967 entity
Predicate hasTradeName P24555 FINISHED
Object Ebanga 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: Ebanga | Statement: [ansuvimab, hasTradeName, Ebanga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ebanga
Context triple: [ansuvimab, hasTradeName, Ebanga]
  • A. Ebanga chosen
    Ebanga is a monoclonal antibody drug used to treat Zaire ebolavirus infection (Ebola virus disease).
  • B. Benina
    Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
  • C. Ewondo
    Ewondo is a Bantu language spoken primarily by the Ewondo people in central Cameroon, including in and around the capital city, Yaoundé.
  • D. Ekondo-Titi
    Ekondo-Titi is a coastal town and commune in Cameroon's Southwest Region, known for its agricultural activities and location near the Ndian River and the Atlantic coast.
  • E. Eyamba
    Eyamba is a prominent clan of the Efik people of southeastern Nigeria, historically associated with leadership and influence in the Old Calabar region.
  • 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_69e5eeba91a081909c04d61d6117da06 completed April 20, 2026, 9:15 a.m.
Created at: April 10, 2026, 12:06 p.m.