Triple

T195379
Position Surface form Disambiguated ID Type / Status
Subject Biogen E3807 entity
Predicate hasProduct P3585 FINISHED
Object Leqembi
Leqembi is an Alzheimer’s disease drug (lecanemab) that targets amyloid-beta plaques to slow cognitive decline in early-stage patients.
 E25439 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: Leqembi | Statement: [Biogen, hasProduct, Leqembi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Leqembi
Context triple: [Biogen, hasProduct, Leqembi]
  • A. Plegridy
    Plegridy is a pegylated interferon beta-1a medication used to treat relapsing forms of multiple sclerosis.
  • B. Kimvita
    Kimvita is a major coastal dialect of Swahili spoken primarily in and around Mombasa, Kenya.
  • C. Vumerity
    Vumerity is an oral prescription medication used to treat relapsing forms of multiple sclerosis in adults.
  • D. Abital
    Abital is one of the lesser-known wives of the biblical King David, mentioned in the Hebrew Bible as the mother of his son Shephatiah.
  • E. Namba
    Namba is a major commercial and entertainment district in Osaka, Japan, known for its bustling nightlife, shopping, and iconic neon-lit streets.
  • 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: Leqembi
Triple: [Biogen, hasProduct, Leqembi]
Generated description
Leqembi is an Alzheimer’s disease drug (lecanemab) that targets amyloid-beta plaques to slow cognitive decline in early-stage patients.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Leqembi
Target entity description: Leqembi is an Alzheimer’s disease drug (lecanemab) that targets amyloid-beta plaques to slow cognitive decline in early-stage patients.
  • A. Plegridy
    Plegridy is a pegylated interferon beta-1a medication used to treat relapsing forms of multiple sclerosis.
  • B. Kimvita
    Kimvita is a major coastal dialect of Swahili spoken primarily in and around Mombasa, Kenya.
  • C. Vumerity
    Vumerity is an oral prescription medication used to treat relapsing forms of multiple sclerosis in adults.
  • D. Abital
    Abital is one of the lesser-known wives of the biblical King David, mentioned in the Hebrew Bible as the mother of his son Shephatiah.
  • E. Namba
    Namba is a major commercial and entertainment district in Osaka, Japan, known for its bustling nightlife, shopping, and iconic neon-lit streets.
  • 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_69a2548debd48190ae3a06d6e65b53c6 completed Feb. 28, 2026, 2:35 a.m.
NER Named-entity recognition batch_69a25969425081908e178db8ba4631c2 completed Feb. 28, 2026, 2:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69a31c91b22c8190a1d04983ae4fd793 completed Feb. 28, 2026, 4:49 p.m.
NEDg Description generation batch_69a31ea66b8881909748ab11d7a4a50a completed Feb. 28, 2026, 4:58 p.m.
NED2 Entity disambiguation (via description) batch_69a31efbe2d48190b528011dfdf764c1 completed Feb. 28, 2026, 4:59 p.m.
Created at: Feb. 28, 2026, 2:41 a.m.