Large language models (LLMs) have demonstrated strong reasoning abilities, but hallucinations and unverifiable intermediate reasoning steps remain key challenges. In knowledge graph question answering (KGQA), entity-relation paths from knowledge graphs (KGs) provide structured and verifiable evidence for grounded reasoning. However, existing KG-enhanced LLM methods commonly linearize all retrieved reasoning paths into textual prompts, introducing noise and underexploiting their ordered structure. To address these limitations, we propose PathSoft, a hybrid soft prompting framework that combines textual path evidence with soft prompts. PathSoft first employs PathMLP to select reliable reasoning paths, and then uses PathSoftAdapter to convert the selected paths into continuous soft prompts while preserving their ordered path structure. Finally, the resulting soft prompt tokens are combined with the textual prompt at the embedding layer of the frozen LLM, enabling it to exploit both readable path evidence and path structure information. Experiments on WebQSP and CWQ show that PathSoft outperforms existing KGQA baselines. The code is available at https://github.com/tengyan1786-blip/PathSoft.