Two new, large, (semi-)automatically annotated datasets were created from experimental procedures that were extracted from patents and patent applications. These datasets were used to train surrogate LLMs on the task of creating action graphs, i.e., simple structured output, from the unstructured input. The LLMs that were trained, a BigBirdPegasus and a Longformer Encoder Decoder model, strike a good balance between performance, generality, and fitness for purpose and can be hosted and run on standard consumer-grade hardware. The generated action graphs were generally of high quality, with some remarkable generalizations especially from the BigBirdPegasus model. Moreover, the training dataset (and hence the trained models) were kept rather broad in scope and their applicability to experimental procedures from different domains was investigated, namely materials science, organic chemistry, inorganic chemistry, and patent literature. Further, the possibility of using a node editor for creating, visualizing, and modifying synthesis workflows for MAPs and SDLs is presented. The node graphs can either be created automatically from the action graphs generated by the LLMs or from scratch by the user. They offer an intuitive way of creating automated synthesis workflows that can be run on robotic hardware without requiring classical programming skills. By dynamically creating the nodes in the node editor from the API of the software backend underlying the MAP or SDL, it is rather straightforward to generate executable code from these node graphs that can then be used to directly run the planned experiment(s) on the hardware platform. Moreover, the nature of the node graph itself as well as the fact that the nodes are dynamically generated from the API and hence allow following the inheritance structure or MRO of the underlying classes in the software backend make it possible to extract knowledge graphs that follow an ontology directly constructed from the API respectively its software architecture. All resources generated in this work, including the training and validation datasets, the fully trained neural networks, and the implementation of the node editor and the scripts for generating, visualizing, and analyzing the automatically generated knowledge graphs are made publicly available. While the concept of creating the action graphs, node graphs, and knowledge graphs is very general, the final implementation and hence also the knowledge graphs and ontologies they follow are domain specific and hardware and software dependent. A common ontology for the different subdomains, such as nanomaterial synthesis, could help in the future to make knowledge graphs and even entire synthesis workflows platform independent and readily interoperable between different MAPs or SDLs with different software backends. Another extension that seems natural in the context of node graphs and the generated knowledge graphs is the storage of the underlying data in graph databases, a step that is also planned to be further investigated in the future.