Partially extracts line 8's schedule metadata: flight_period_start, flight_period_end, frequency, and days_pattern are null even though the source row and other records contain scheduling information.
What was measured
Semantic Field Enrichment
Are derived fields such as transaction_type, transaction_id, cheque_number, day patterns, and ad codes correctly classified or extracted beyond raw OCR?
decisive for this rankingtransformation
For this ranking, correctly identifying derived business fields is part of producing usable structured data, not just a nice extra. (3 of 3 judges)
What was given, what came back
Test input: Invoice PDF · pdf · group: business-document-extraction
Input — what we sent
A two-page broadcast advertising invoice PDF with nested header metadata, billing and remit addresses, and eight line items spanning a page break. It was used to stress hierarchical line-item extraction, amount precision, time/day parsing, code extraction, and summary validation.
Why this input is hard
- · Nested line-item hierarchy extraction
- · Multi-page continuity across a page break
- · Precision on large dollar amounts and totals
- · Parsing complex time slots and day patterns
- · Extraction of Ad IDs and reconciliation codes
- · Mapping structured metadata sections correctly
- · Financial summary validation
- · Handling political advertising compliance text
Output — unretouched
Also checked on this input — same tool, 3 other criteria
Extraction Accuracy✓ WorkedExtracts invoice and finance values accurately, including invoice number 4064621-1 and summary totals of 8 aired spots, 29750 gross, 4462.5 commission, and 25287.5 net amount due.Schema Adherence✓ WorkedPreserves the requested invoice hierarchy, populating invoice_metadata, advertiser, station, account_details, billing_address, remit_address, flight_dates, line_items, and summary as separate JSON objects rather than flattening the page.Table & Record Completeness✓ WorkedKeeps all 8 advertising line items as separate records with no row merging or duplication.
Provenance
- Observation
- 96339391-c5f5-446c-8ee6-82afe8577102
- Evidence run
- a061b9e7-a9c5-443d-a171-b296aaf51b8c
- Study
- Extract and query structured data from documents using natural language
- Research task
- 86b9y25e5
- Tested at
- not recorded
- Source
- first-party
- Evidence state
- verified
- Proof shown
- input + output shown
- Cost / latency
- not captured
- Repeat run
- not captured
- Tester
- not captured
The last three rows are honest blanks, not placeholders — our capture has no field for them yet.
Query this
get_evidence({
tool: "nanonets",
scenario: "business-document-extraction"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 8 other tools
measured on Semantic Field Enrichment
Datalab✓ WorkedCaptures derived invoice line-item fields correctly, including time_slot 9a-10a, day_of_week Su, air_time 9:38 AM, and ad_id NRCCWI071005 on line item 2.Docsumo✓ WorkedThe tool correctly splits compound scheduling data for all 8 line items, with Day of Week and Day Pattern both populated correctly instead of being garbled together.Extend AI✓ WorkedThe tool correctly derives higher-level line-item fields such as `day_of_week: Tu` and `days_pattern: -T----` alongside ad ID, reference number, and flight-period dates.Landing AI✓ WorkedPopulates derived scheduling fields such as day_of_week "M" and days_pattern "MTWT" on invoice line items.LlamaParse✓ WorkedThe tool correctly extracts derived scheduling and coding fields for invoice line items, including day_of_week, days_pattern, air_time, ad_id, time_slot, and flight-period references.Reducto✓ WorkedCorrectly derives higher-level invoice metadata such as invoice_month, invoice_period_start, and invoice_period_end alongside invoice_number and order_number, showing semantic field extraction beyond raw OCR text.Retab✓ WorkedDerives scheduling fields from the line item, including day_of_week 'Su', days_pattern '------S', air_time '09:38:00', and time_slot '9a-10a'.Unstract⚠ StruggledMis-reproduces the fixed-width day_of_week scheduling mask, with examples like MTWT- for source MTWT--- and F- - for source ----F--; the report says days_pattern is the more reliable field.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com