"We present a strong and practical pipeline system for Mandarin spoken entity and relation extraction (Spoken-ERE), which integrates an industrial-grade ASR module (FireRedASR) with a span-based joint entity-relation extraction model. Unlike recent approaches that rely on large language models (LLMs) for end-to-end spoken information extraction, our method uses a modular pipeline design that is lightweight, interpretable, and easy to deploy. Despite its simplicity,our system achieves top-tier performance in a recent shared task workshop, outperform-ing several 5× larger LLM-based systems for 20% on F1-score. We demonstrate through experiments that with robust ASR and a well-designed span-based model, classical pipelines re-main competitive and, in some scenarios, even preferable to LLM-based solutions for spoken information extraction in Mandarin."