Fix PR feedback: remove duplicate media folder and change numbered lists to bullet points

Author: theresa-i

learn-pr/wwl-data-ai/create-ontology-with-fabric-iq/includes/3-build-ontology-components-manually.md

@@ -38,11 +38,11 @@ To add properties to the entity, select **Add properties** to open the propertie
 
 For each property, you make three critical decisions:
 
-1. **Property name** — Describes what the property represents. Use specific terms like `HospitalName` rather than generic terms like `Name`, so queries are unambiguous when the same concept appears across multiple entity types such as `HospitalName`, `DepartmentName`, `PatientName` are each clearly distinct. Property names often match the column names in your source data—when your source tables already use business-friendly names, you can use the same name for both the property and the source column. Property names must be 1–26 characters, contain only alphanumeric characters, hyphens, and underscores, and start and end with an alphanumeric character.
+- **Property name** — Describes what the property represents. Use specific terms like `HospitalName` rather than generic terms like `Name`, so queries are unambiguous when the same concept appears across multiple entity types such as `HospitalName`, `DepartmentName`, `PatientName` are each clearly distinct. Property names often match the column names in your source data—when your source tables already use business-friendly names, you can use the same name for both the property and the source column. Property names must be 1–26 characters, contain only alphanumeric characters, hyphens, and underscores, and start and end with an alphanumeric character.
 
-1. **Data type** — Defines what kind of values the property holds. String properties store text like names and addresses. Integer properties store whole numbers like floor numbers or patient counts. The data type must match the source data type when you bind the ontology to data sources later. Binding connects your ontology definitions to actual data in lakehouse tables or eventhouse streams, populating entity instances with real values.
+- **Data type** — Defines what kind of values the property holds. String properties store text like names and addresses. Integer properties store whole numbers like floor numbers or patient counts. The data type must match the source data type when you bind the ontology to data sources later. Binding connects your ontology definitions to actual data in lakehouse tables or eventhouse streams, populating entity instances with real values.
 
-1. **Property type** — Defines whether the property represents static or time series data. Static properties describe attributes that change infrequently—a hospital's name, a department's floor number, a patient's date of birth. Time series properties represent continuously arriving observations, like vital signs readings that arrive every few seconds from monitoring equipment. All of Lamna Healthcare's entity types in this unit use static properties. You'll encounter time series properties in a later unit when you add the VitalSignsReadings entity type and connect it to eventhouse streaming data.
+- **Property type** — Defines whether the property represents static or time series data. Static properties describe attributes that change infrequently—a hospital's name, a department's floor number, a patient's date of birth. Time series properties represent continuously arriving observations, like vital signs readings that arrive every few seconds from monitoring equipment. All of Lamna Healthcare's entity types in this unit use static properties. You'll encounter time series properties in a later unit when you add the VitalSignsReadings entity type and connect it to eventhouse streaming data.
 
 ## Add properties to entity type
 

learn-pr/wwl-data-ai/create-ontology-with-fabric-iq/includes/4-generate-ontology-from-semantic-model.md

@@ -30,8 +30,8 @@ Generation handles several tasks automatically, saving significant manual effort
 
 After generation, complete these tasks to finalize your ontology:
 
-1. **Review entity type keys** — Verify each entity type has a key defined. Keys are inferred from the semantic model's relationship structure, and most are configured automatically, but you should verify they're all present.
+- **Review entity type keys** — Verify each entity type has a key defined. Keys are inferred from the semantic model's relationship structure, and most are configured automatically, but you should verify they're all present.
 
-2. **Verify and configure relationship types** — Relationship bindings are created based on the semantic model structure. Depending on how the model defines relationships, some bindings are configured automatically while others require manual configuration to make them queryable.
+- **Verify and configure relationship types** — Relationship bindings are created based on the semantic model structure. Depending on how the model defines relationships, some bindings are configured automatically while others require manual configuration to make them queryable.
 
-3. **Add time series bindings for streaming data** — Static entity bindings from the lakehouse are configured automatically. For entity types that need real-time streaming data, add time series bindings to connect eventhouse data sources.
+- **Add time series bindings for streaming data** — Static entity bindings from the lakehouse are configured automatically. For entity types that need real-time streaming data, add time series bindings to connect eventhouse data sources.