An abstracted multi-phase, multi-model linkage strategy is presented via an operational workflow applied within a real-world multi-dataset environment, with population-scale datasets spanning multiple domains. The approach provides a consistent, repeatable playbook for data linkage, leveraging the pipeline in producing quality linked data for research in a robust and scalable manner. At the abstract level, we define building blocks to produce linked cohorts from component models, merging identified sub-graphs into larger cohorts in a systematic pipeline from data acquisition to cohort provisioning. First, each dataset is intra-linked and quality checked, leveraging dataset-specific information to produce high quality within-dataset links. Next, inter-dataset links are produced, utilising common identifiers between sets. Both intra- and inter-set linking phases make use of deterministic and probabilistic linkage methods to produce a comprehensive set of edges. In the final phase, Intra- and inter-set edges are integrated into a single multi-set linked cohort before versioning and release. Producing a larger cohort from dataset-specific sub-graphs allowed the exploitation of focuse
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