Knowledge Graphs (KG) form the backbone of many knowledge dependent applications, such as search engines and digital personal assistants. When constructing a KG, data can be manually curated by experts, contributed by volunteers, automatically extracted using hand-crafted or learned rules, or generated from unstructured text via machine learning techniques. Regardless of the approach, anomalies are inevitable, as no data source is perfect. To address this, we propose ETCOD, an embedding-based anomaly detection approach for KG validation and quality enhancement, combined with Large Language Models (LLM) for explanation and verification. First, we generate semantic embeddings of triples in order to capture entity and relation similarities. Next, we perform pattern mining to identify anomalous triples. Finally, detected anomalies are forwarded to an LLM to provide human-understandable explanations and reasoning. We conducted experiments on real-world KGs, including YAGO-1 and YAGO-4.5, using ChatGPT-4o and Gemini for explanation. Our analysis highlights which types of anomalies are most effectively explained by each model and where they tend to fall short. The results demonstrate that embedding-based detection is effective in identifying anomalies, while LLMs enhance interpretability by providing context-aware explanations.