This paper proposes a novel architecture for distributed, traceable, and event-driven execution of LLM-related tasks by combining W3C Linked Data Notifications (LDN) with remote Python scripts executed via uv run. This architecture enables any AI task-especially inference- to be executed locally with no software installation, traced via interoperable notifications, and archived with full provenance metadata (e.g., models, parameters, etc.). To achieve this, the system leverages LDN as a semantic pub-sub orchestration layer, combined with uv-based scripts as reproducible, stateless microservices. We demonstrate the value of this architecture for building transparent, auditable, and distributed Large Language Model (LLM) inference workflows with three working proof-of-concepts: (1) a basic semantically-notified inference where notifications populate a register of evidences for transparency, (2) a Retrieval-Augmented Generation (RAG) pipeline triggered by Create events and executed through script-based stages, and (3) a distributed inference setup where task-specific SLMagents independently process jobs and respond via Announce messages. Each stage archive full provenance metadata (model version, script SHA, parameters, runtime) using PROV-O, supporting reproducibility and auditability. This architecture lays the groundwork for a lightweight, decentralized, and FAIR-aligned standard for orchestrating LLM tasks.