Triple

T1351590
Position Surface form Disambiguated ID Type / Status
Subject Fampyra E28893 entity
Predicate developedBy P73 FINISHED
Object Biogen Idec E3807 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: Biogen Idec | Statement: [Fampyra, developedBy, Biogen Idec]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Biogen Idec
Context triple: [Fampyra, developedBy, Biogen Idec]
  • A. Biogen chosen
    Biogen is a major American biotechnology company known for developing therapies for neurological and neurodegenerative diseases.
  • B. Eisai and Biogen
    Eisai and Biogen are pharmaceutical companies that collaborate on developing innovative therapies, particularly in the field of neurodegenerative diseases such as Alzheimer’s.
  • C. Genentech
    Genentech is a pioneering American biotechnology company known for developing groundbreaking therapies and being one of the first firms to apply genetic engineering to medicine.
  • D. Alkermes
    Alkermes is a biopharmaceutical company that develops innovative medicines for central nervous system disorders and other serious chronic diseases.
  • E. Eli Lilly and Company
    Eli Lilly and Company is a major American pharmaceutical corporation known for developing and manufacturing a wide range of prescription medicines, including treatments for diabetes, cancer, and mental health disorders.
  • 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_69a498571d248190a0ac9eb02d97097f completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c26b1b4881908ae4b1b2c9b268a0 completed March 1, 2026, 10:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69acc63eef908190aef058396f63a5a4 completed March 8, 2026, 12:43 a.m.
Created at: March 1, 2026, 7:56 p.m.