Pretrained language models (LMs) have significantly advanced a variety of semantic tasks and have shown promise as sources of knowledge elicitation. While prior work has studied this ability through probing or prompting, the potential of LMs for large-scale knowledge base construction remains underexplored. The fourth edition of the LM-KBC Challenge invited participants to build knowledge bases directly from LMs, given specific subjects and relations. Unlike existing probing benchmarks, the challenge imposed no simplifying assumptions on relation cardinality-allowing a subject entity to be linked to zero, one, or multiple object entities. To ensure accessibility, the challenge featured a single track based the same LLM to be used by all participants. Five submissions were received, which explored a variety of ideas from self-consistency, self-RAG, reasoning, and prompt optimization.