Contextual biasing in end-to-end (E2E) models is challenging because E2E models do poorly in proper nouns and a limited number of candidates are kept for beam search decoding. This problem is exacerbated when biasing towards proper nouns in foreign languages, such as geographic location names, which are virtually unseen in training and are thus out-of-vocabulary (OOV). While a grapheme or wordpiece E2E model might have a difficult time spelling OOV words, phonemes are more acoustically oriented, and past work has shown that E2E models can better predict phonemes for such words. In this work, we address the OOV issue by incorporating phonemes in a wordpiece E2E model, and perform contextual biasing at the phoneme level to recognize foreign words. Phonemes are mapped from the source language to the foreign language and subsequently transduced to foreign words using pronunciations. We show that phoneme-based biasing performs 16% better than a grapheme-only biasing model, and 8% better than the wordpiece-only biasing model on a foreign place name recognition task, while causing slight degradation on regular English tasks.