The knowledge ladder explains why the v0.5.0 pipeline grew two new information layers (Stage 2.7 phrase grouper, expanded Stage 5 reconcile). This article walks through what they actually do on one concrete input, end-to-end.
A Conditional Random Field (CRF) is a structured-prediction layer that sits on top of a per-token classifier and finds the best whole-sequence assignment of labels, subject to learnable transition rules between consecutive labels.
Mailwoman v2 (current as of May 2026) runs addresses through a six-stage pipeline. Each stage adds one kind of knowledge the stages below it cannot easily derive. Rule classifiers and a neural classifier coexist. A policy registry decides whose vote wins for each address component.
A short article on the one non-obvious knob inside Stage 5's joint decoder. If you skip it the reconciler converges on the empty parse for every input.
The staged pipeline is a contract for decomposition by what each layer knows. Every stage is the rightful home of a particular kind of information; pushing work to the wrong stage produces fragile systems that try to learn things from data that they could have looked up, or look up things that they could have learned. This article catalogues the layers, what each one knows, and the two layers we don't ship yet but should.
You don't have to take Mailwoman's pipeline as-is. The runtime coordinator (createRuntimePipeline) accepts each of the six stages as an injectable function or interface; an integrator can swap any of them for a custom implementation without forking the core.
Reading Addresses that break geocoders makes one thing obvious: no single model handles every failure class well. Different failures want different fixes. Some want preprocessing rules, some want a small classifier, some want a transformer, some want a resolver that returns candidates instead of pretending to be sure.
Traditional address parsers split the input into tokens, classify each token independently, then try to reassemble the pieces into a coherent parse. This sequence contains a structural circularity: you cannot group tokens correctly without knowing their types, and you cannot type them correctly without knowing their groups. The traditional architecture resolves this with heuristics, exceptions, and solver post-processing. The exception pile grows without bound.
In Mailwoman's architecture, a concordance is the resolver's answer to the question: "Do these parsed components form a coherent place in the real world?" It is the mechanism that prevents the parser from emitting a parse that is structurally valid but geographically impossible — like "Paris, Texas" labelled as "Paris, Île-de-France" or "NY-NY Steakhouse, Houston TX" with "NY" tagged as a region.