Pine Property Services: Claim register JSON
Claim register JSON for Pine Property Services. The same 3 claims with the three column totals hoisted into a totals object, so a reader can check its own aggregation against the file rather than only against another file.
json
application/json
- Document Set
- insurance
- Industry
- trades-property
- Source Kit
- trades-property
- Entity
- Pine Property Services
- Synthetic
- true
- As Of
- 2026-09-08
Binary json: no in-browser preview. Download it above to open in a compatible application.
Specifications
- Document Set
- insurance
- Industry
- trades-property
- Source Kit
- trades-property
- Entity
- Pine Property Services
- Synthetic
- true
- As Of
- 2026-09-08
- Records
- 3
- Has Totals Object
- true
Testing contract
Expected to pass- Scenario
- Aggregate the claims array and compare with the totals object.
- Expected result
- The aggregation gives 17890.00 paid, 2760.00 reserved and 20650.00 incurred, which is exactly what the totals object holds.
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“Pine Property Services: Claim register JSON” is a deterministic Novus Examples fixture for Data import, JSON parsing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 3 records. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
Data fixtures document their exact quirks (delimiters, encodings, null handling, schema, and row counts) in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.
Code examples
import json
with open("claim-register.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
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Generated by generation/industry_documents_two.py. Free for any use, no attribution required, license.