The paper proves that latent concepts can be identified in unsupervised learning through functional orthogonality, an orthogonality constraint on the generative mapping's Jacobian. This condition enables identifiability in general nonlinear models without needing statistical independence or causal assumptions, as long as the latent domain supports all factor combinations. Experiments with normalizing flows confirm reliable recovery of true factors, offering a viable foundation for disentangled representation learning.