Triple

T6004715
Position Surface form Disambiguated ID Type / Status
Subject onasemnogene abeparvovec E133681 entity
Predicate developedBy P73 FINISHED
Object Novartis E141505 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: Novartis | Statement: [onasemnogene abeparvovec, developedBy, Novartis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Novartis
Context triple: [onasemnogene abeparvovec, developedBy, Novartis]
  • A. Novartis chosen
    Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
  • B. Roche
    Roche is a common surname of French origin borne by various notable individuals across fields such as architecture, politics, and the arts.
  • C. Roche
    Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
  • D. Sanofi
    Sanofi is a major French multinational pharmaceutical company known for developing prescription medicines, vaccines, and consumer healthcare products worldwide.
  • E. Pfizer
    Pfizer is a major American multinational pharmaceutical and biotechnology corporation known for developing a wide range of prescription medicines and vaccines, including one of the first widely used COVID-19 vaccines.
  • 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_69c00872444c8190bfaf1739dcec765c completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c04f10d18081908c351170b7f58d3d completed March 22, 2026, 8:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c1088f5c84819094e4696c24c4dd79 completed March 23, 2026, 9:31 a.m.
Created at: March 22, 2026, 4:06 p.m.