To serve global users safely and productively, LLMs need culture-specific knowledge that might not be learned during pre-training. How do we find knowledge that is (1) salient to in-group users, but (2) unknown to LLMs? The most common solutions are single-initiative: either researchers define challenging questions that users passively answer (traditional annotation), or users actively produce data that researchers structure as benchmarks (knowledge extraction). The process would benefit from mixed-initiative collaboration, where users guide the process to meaningfully reflect their cultures, and LLMs steer the process to meet the researcher’s goals. We propose CultureCartography as a methodology that operationalizes this mixed-initiative vision. Here, an LLM initializes annotation with questions for which it has low-confidence answers, making explicit both its prior knowledge and the gaps therein. This allows a human respondent to fill these gaps and steer the model towards salient topics through direct edits. We implement Culture Cartography as a tool called Culture Explorer. Compared to a baseline where humans answer LLM-proposed questions, we find that Culture Explorer more effectively produces knowledge that strong models like DeepSeek R1, Llama-4 and GPT-4o are missing, even with web search. Fine-tuning on this data boosts the accuracy of Llama models by up to 19.2% on related culture benchmarks.