The field of knowledge graph construction and hallucination detection is rapidly advancing, with a focus on improving the quality and reliability of generated knowledge graphs. Recent developments have led to the creation of more efficient and effective pipelines for constructing knowledge graphs, such as those that utilize large language models and ontology-aware approaches. Additionally, there is a growing emphasis on detecting and mitigating hallucinations in generated text, particularly in high-stakes applications such as legal and regulatory domains. Noteworthy papers in this area include Wikontic, which proposes a multi-stage pipeline for constructing high-quality knowledge graphs, and HalluGraph, which introduces a graph-theoretic framework for auditable hallucination detection. Other notable papers include Graphing the Truth, which presents a framework for structured visualizations of knowledge graphs to detect hallucinations, and OntoMetric, which proposes an ontology-guided framework for automated ESG knowledge graph construction.