In Japanese, loanwords are primarily written in Katakana, a syllabic writing system, based on their pronunciation. However, the transliterated loanwords often exhibit spelling variations, such as the word “Hepburn” being written as “ヘボン (hebon)”, “ヘプバーン (hepubaan)”, “ヘップバーン (heppubaan)”. These orthographical variants pose a bottleneck in multilingual Named Entity Recognition (NER), because named entities (NEs) do not have one-to-one matches. In this study, we introduce a rule-based grapheme-to-phoneme (G2P) system for Japanese based on literature in linguistics and a large-scale multilingual NE dataset with annotations of the International Phonetic Alphabet (IPA), focusing on IPA to address the Katakana spelling variations in loanwords. These rules and dataset are expected to be beneficial for tasks such as NE aggregation, G2P system, construction of cross-lingual language models, and entity linking. We hope our work advances research on Japanese NER with multilingual loanwords by solving the spelling ambiguities.