[Bug] RDF: Adding resource to the canvas is very slow or fails with out of memory

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**Describe the bug**
On a larger RDF database, when I add a resource from the search panel to the canvas it can take anywhere from 30 seconds to 10 minutes to complete. During this time no indication is given to the user that something is happening.

- OS: macOS 14.3.1
- Browser: Arc (Google Chromium)
- Graph Explorer Version: 1.5.1
- Graph Database & Version: Amazon Neptune

**To Reproduce**
Steps to reproduce the behavior:
1. Connect to a large RDF database with SPARQL
2. Search for a resource with many neighbors or relationships
3. Add that resource to the canvas
4. Observe nothing happening in the UI for a long time

You can see the pending request in the browser's network tab.

**Slow response**

In this example, the request took 1.4 min to complete:


And here you can see there are not that many neighbors:


And here is the query that was executed:

```sparql
SELECT ?class (COUNT(?class) AS ?count) {
?subject a ?class {
SELECT DISTINCT ?subject ?class {
?subject a ?class .
{ ?subject ?p }
UNION
{ ?p ?subject }
}
LIMIT 500
}
}
GROUP BY ?class
```

**Out of Memory Error**

The query that was executed was:

```sparql
SELECT ?class (COUNT(?class) AS ?count) {
?subject a ?class {
SELECT DISTINCT ?subject ?class {
?subject a ?class .
{ ?subject ?p }
UNION
{ ?p ?subject }
}
LIMIT 500
}
}
GROUP BY ?class
```

This resulted in an out of memory error:

```json
{
"error": {
"status": 500,
"message": "\n{\n \"detailedMessage\": \"Operation terminated (out of memory)\",\n \"requestId\": \"38d41423-0bb8-446a-8d11-4de1ee8cfb24\",\n \"code\": \"MemoryLimitExceededException\",\n \"message\": \"Operation terminated (out of memory)\"\n}"
}
}
```

**Expected behavior**
Adding a single resource to the canvas should not be slow or cause errors.

Start at the search panel's add-to-canvas flow and reproduce against a large RDF database such as Amazon Neptune. Inspect the pending browser network request and the generated SPARQL query, then trace the request path to identify the slow or memory-intensive operation. Done means adding one resource completes promptly without an out-of-memory error and gives the user progress feedback.

Written by the indexing model from the issue text.