Triple

T4784968
Position Surface form Disambiguated ID Type / Status
Subject mAb114 E106453 entity
Predicate hasBrandName P40804 FINISHED
Object Ebanga
Ebanga is a monoclonal antibody drug used to treat Zaire ebolavirus infection (Ebola virus disease).
 E468968 NE FINISHED

How this triple was built (4 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: [mAb114, hasBrandName, Ebanga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ebanga
Context triple: [mAb114, hasBrandName, Ebanga]
  • A. Benina
    Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
  • B. Ewondo
    Ewondo is a Bantu language spoken primarily by the Ewondo people in central Cameroon, including in and around the capital city, Yaoundé.
  • C. Eyamba
    Eyamba is a prominent clan of the Efik people of southeastern Nigeria, historically associated with leadership and influence in the Old Calabar region.
  • D. Dutsin-Ma
    Dutsin-Ma is a town in northern Nigeria known for hosting the Federal University Dutsin-Ma and serving as an important local commercial and educational center.
  • E. Cacongo
    Cacongo is a coastal town and municipality in Angola’s oil-rich Cabinda exclave, known historically as a trading port on the Atlantic coast.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Ebanga
Triple: [mAb114, hasBrandName, Ebanga]
Generated description
Ebanga is a monoclonal antibody drug used to treat Zaire ebolavirus infection (Ebola virus disease).
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ebanga
Target entity description: Ebanga is a monoclonal antibody drug used to treat Zaire ebolavirus infection (Ebola virus disease).
  • A. Benina
    Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
  • B. Ewondo
    Ewondo is a Bantu language spoken primarily by the Ewondo people in central Cameroon, including in and around the capital city, Yaoundé.
  • C. Eyamba
    Eyamba is a prominent clan of the Efik people of southeastern Nigeria, historically associated with leadership and influence in the Old Calabar region.
  • D. Dutsin-Ma
    Dutsin-Ma is a town in northern Nigeria known for hosting the Federal University Dutsin-Ma and serving as an important local commercial and educational center.
  • E. Cacongo
    Cacongo is a coastal town and municipality in Angola’s oil-rich Cabinda exclave, known historically as a trading port on the Atlantic coast.
  • F. None of above. chosen

Provenance (5 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_69bd43f4a9588190bf73e20bc27c03cc completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd65ae49ec81908f16248d22d1155f completed March 20, 2026, 3:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69be43dbdec88190817845e7930a18f6 completed March 21, 2026, 7:08 a.m.
NEDg Description generation batch_69be447c21d48190ab57c8761e733ff4 completed March 21, 2026, 7:10 a.m.
NED2 Entity disambiguation (via description) batch_69be45f5ebec8190b62c428b465d1bd9 completed March 21, 2026, 7:17 a.m.
Created at: March 20, 2026, 1:22 p.m.