Another potential factor is that prompt injections as a concept are salient to our models: sampling from GPT-6 Astra with no input or system prompt often returns reports on prompt injections.

Conceptual data model: describes the semantics of a domain, being the scope of the model. For example, it may be a model of the interest area of an organization or industry. This consists of entity classes, representing kinds of things of significance in the domain, and relationship assertions about associations between pairs of entity classes. A conceptual schema specifies the kinds of facts or propositions that can be expressed using the model. In that sense, it defines the allowed expressions in an artificial 'language' with a scope that is limited by the scope of the model.

Creating a community network ontology is therefore about much more than just knowledge representation. It also requires us to think about how this conceptual knowledge model affects real-world knowledge creation and application processes, in our case concerning participatory community network mapping. Its participatory nature means that we need to think hard about how to explicitly involve the community in the construction, evolution, and use of the ontology.