The diversity and highly structured nature of the information explicitly provided to the LLM significantly complicate the construction of hard prompts. Building on prior research in event extraction and knowledge injection for LLMs, we will explore various representation strategies. One direction involves comparing formal representations of relationship schemas (including argument types and relationship labels) and explicit Knowledge Base (KB) information, with their verbalized counterparts. Furthermore, the ordering of prompt components, whether by semantic similarity or intrinsic nature, has emerged as a critical factor and will be rigorously assessed.