These are clear and useful explanations, but I can think of two aspects that might be too narrow a view...
1. In modeling, you can think of what the data "is", or what the data is "about". There is a difference between a data item that is composed of other data items, and a domain object that is composed of other domain objects. The data items may be assembled in ways that don't necessarily parallel the domain.
2. When considering history and traceability, deletion of domain objects doesn't necessarily delete the parts, just the relationship between those parts and the whole. An engine might be traced (even given identity with an OID) from manufacture, then assembled into an identifable truck, then removed, reworked, and made available for use in another and different truck. In certain domains keeping traceability of the parts, and even the batches of material used to form them, is critical (e.g., medical implants, nuclear power plants etc.). As more organizations seriously consider having continuous improvement programs, and data-driven decision making, there is an even greater call for building history and traceability into the models and data systems.