| 32 | | '''Appearance''' can be understood as abstract means to determine the appearance form of persons to which the data set relates (=ownership), which can be types of "Individual", "Group", "Body" or "Personal Effects" or "Relationship". |
| 33 | | |
| 34 | | ''This axis does not describe the actually observed appearance form of a person, which indeed can be multiple at the same time.'' |
| 35 | | |
| 36 | | ==== Distinction ==== |
| 37 | | |
| 38 | | '''Distinction''' can be understood as any features which are used to distinguish particular persons (or groups, bodies...) in operations. |
| 39 | | |
| 40 | | ''It is important to understand that these features are always assigned, i.e. not naturally given - they cannot be observed but have to be communicated.'' |
| 41 | | |
| 42 | | The most commonly used distinction feature for persons is their name, but also identity (=an assigned unique number or character string). |
| 43 | | |
| 44 | | ==== Finding ==== |
| | 32 | '''Distinction''' can be understood as any features which are used to distinguish particular persons (or groups, bodies...) in operations. ''It is important to understand that these features are always assigned, i.e. not naturally given - they cannot be observed but have to be communicated.'' The most commonly used distinction feature for persons is their name, but also identity (=an assigned unique number or character string). |
