The blueprint-versus-building metaphor is literal for us, so here is what it looks like when the building is an actual building.

ML Systems' Collective Ontology is the blueprint: what kinds of things exist in a house (rafter, plate, sheathing, footing) and how they are allowed to relate (sits on, ties to, is fastened through by). The knowledge graph is one specific house filled in from a town assessor record, a homeowner, imagery, and seven AI agents. The part your table calls the Kinetic layer is where it gets interesting, because our Action is taking the house apart. The scheduler compiles the ontology's edges into a job DAG, the crew executes it as a cut plan, and every member that comes free writes a MEASURED claim back into the graph. That is the "executes and learns" loop, except the sensor is a crew with a saw, and what the building says back corrects what the record, the homeowner and the model each believed was up there.

One difference from the Palantir shape worth naming. Object Type / Link Type / Action Type assumes the instance data is a fact once written; the writeback strategies (user edits always win, timestamp) exist to settle collisions. Our knowledge graph stores claims, not facts: each node value carries its source, an evidence grade (MEASURED > STATED > RECORD > MODELED) and a verification state, authority is scoped by domain rather than global, and two credible sources that disagree resolve to a conflict state that stays visible. The blueprint is a contract about meaning; the building is a set of competing descriptions until somebody measures it.

The last step in your stack, execution feeding back into learning, is what the ontology is for in our case: each deconstruction produces a fully recorded disassembly sequence, and the ontology grows by extending its codes from that record rather than by changing its storage.

github.com/MLSystemsRI/ml-systems-public