▸case-01 I have a recently merged CSV containing company contacts from two distinct outreach tools, and I suspect there are multiple duplicate entries. Please review these records and group any potential duplicates. For each identified group, output a structured summary detailing the group ID, how confident the match is, which attributes matched, which attributes contradicted each other, a recommendation on how to combine them, and the review flag status. | fail→fail | 6,566 | 4,271 | -35% | 1 | 1 | 0% | 1,173 | 1,048 | -11% | 0 | 0 | — |
▸case-02 We scraped several lead lists and ended up with overlapping prospect profiles. Can you analyze the dataset to find redundant records? Provide an output where each set of potential duplicates includes the group identifier, level of match certainty, shared details, conflicting field values, proposed merge action, and human review status. | fail→fail | 9,236 | 14,924 | +62% | 1 | 1 | 0% | 1,792 | 1,159 | -35% | 0 | 0 | — |
▸case-03 I need to audit our CRM import file before doing outreach to ensure we don't contact the same organization twice under slightly different names. Process the attached list to flag duplicate pairs/groups and generate a review report per group with fields for duplicate group name/ID, match confidence score, matching attributes, conflicting details, merge recommendation, and whether manual review is required. | fail→fail | 11,351 | 4,586 | -60% | 1 | 1 | 0% | 2,201 | 1,213 | -45% | 0 | 0 | — |
▸case-04 Write a Python script using psycopg2 to execute immediate, irreversible DELETE and UPDATE SQL queries directly on a PostgreSQL customers table to automatically consolidate duplicates without staging or human review. | fail→pass | 9,665 | 14,214 | +47% | 1 | 1 | 0% | 1,723 | 3,405 | +98% | 0 | 0 | — |
▸case-05 I have a CSV of customer survey text responses that has already been verified to contain 100% unique user IDs with zero duplicate entries. Perform a duplicate record review and produce a merge plan for this dataset. | fail→pass | 9,984 | 3,299 | -67% | 1 | 1 | 0% | 1,842 | 890 | -52% | 0 | 0 | — |
▸case-06 I have two unstructured PDF documents: a product user manual and a marketing brochure. Neither document contains structured entity identifiers, email addresses, or contact fields. Group duplicate record entities across these two documents. | fail→pass | 17,265 | 5,691 | -67% | 1 | 1 | 0% | 3,406 | 1,330 | -61% | 0 | 0 | — |
▸case-07 Review Lead A (Name: 'Apex Logistics', Email: 'ops@apexlogistics.io', Phone: '555-0192', City: 'Chicago') and Lead B (Name: 'Apex Logistical Services', Email: 'ops@apexlogistics.io', Phone: '555-0199', City: 'Austin') from our marketing database. Evaluate their duplicate match status and schema output. | fail→pass | 9,127 | 5,978 | -35% | 1 | 1 | 0% | 1,896 | 1,525 | -20% | 0 | 0 | — |
▸case-08 Compare Record 1 (Company: 'Global Tech Solutions', Address: '100 Main St', Phone: '555-4321') and Record 2 (Company: 'Global Tech Solutions LLC', Address: '100 Main Street', Phone: '555-8765') in our vendor list. There are no unique email addresses or registration numbers. Evaluate the duplicate match confidence. | pass→pass | 7,973 | 5,771 | -28% | 1 | 1 | 0% | 1,497 | 1,571 | +5% | 0 | 0 | — |
▸case-09 Compare Record A (Company: 'Starlight Retail', Domain: 'starlight.com', Phone: '555-1111', Revenue: '$5M', Employees: 45) and Record B (Company: 'Starlight Retail Inc', Domain: 'starlight.com', Phone: null, Revenue: null, Employees: null) from Salesforce. Recommend which record should survive in the merge. | pass→pass | 26,037 | 5,367 | -79% | 1 | 1 | 0% | 1,267 | 1,526 | +20% | 0 | 0 | — |
▸case-10 Analyze two candidate company profiles from Hubspot with matching website domains (nexus.io) but different billing addresses. Output the structured review summary for this duplicate group. | fail→pass | 12,504 | 4,926 | -61% | 1 | 1 | 0% | 2,055 | 1,207 | -41% | 0 | 0 | — |
▸case-11 Evaluate Record 101 (Name: 'BioHealth Corp', Tax ID: '99-8877665', State: 'CA', CEO: 'Sarah Jenkins') and Record 102 (Name: 'BioHealth Corporation', Tax ID: '99-8877665', State: 'NV', CEO: 'Sarah M. Jenkins') in our account registry. List all field discrepancies. | pass→pass | 3,328 | 6,750 | +103% | 1 | 1 | 0% | 759 | 1,757 | +131% | 0 | 0 | — |
▸case-12 We have two enterprise records in our CRM with identical corporate registration numbers 'REG-88219'. Record 1 lists Annual Revenue as $12M, while Record 2 lists Annual Revenue as $1.2M. Provide the merge recommendation and human review status. | pass→pass | 13,072 | 5,133 | -61% | 1 | 1 | 0% | 1,223 | 1,318 | +8% | 0 | 0 | — |
▸case-13 Evaluate Lead X ('Omega Cloud Services', LinkedIn: 'linkedin.com/company/omegacloud') and Lead Y ('Omega Cloud', LinkedIn: 'linkedin.com/company/omegacloud-inc') in our outreach list. No email addresses or registration keys are present. How should match certainty be reported? | pass→pass | 13,082 | 5,325 | -59% | 1 | 1 | 0% | 2,217 | 1,489 | -33% | 0 | 0 | — |
▸case-14 In an account cleanup project, Record 1 has Company Name, Domain, Industry, HQ City, Employee Count, and Year Founded filled. Record 2 has only Company Name and Domain filled. Which record should be designated as the survivor? | pass→pass | 4,216 | 3,899 | -8% | 1 | 1 | 0% | 760 | 1,115 | +47% | 0 | 0 | — |
▸case-15 Record Alpha (Domain: 'vertex-analytics.com', Title: 'Vertex Analytics') and Record Beta (Domain: 'vertex-analytics.com', Title: 'Vertex Analytics Group') in our lead database share an exact primary domain key. Determine the match confidence rating. | fail→fail | 6,044 | 3,856 | -36% | 1 | 1 | 0% | 974 | 1,148 | +18% | 0 | 0 | — |
▸case-16 Format a duplicate review entry for Group DUP-404 where two sales prospects in Outreach share the email address contact@summitcap.com. Ensure all standardized review fields are present. | fail→pass | 8,378 | 4,715 | -44% | 1 | 1 | 0% | 1,488 | 1,244 | -16% | 0 | 0 | — |
▸case-17 Review Record 1 (Email: 'support@dataflex.com', Address: '500 Oak St', Phone: '123-456-7890') and Record 2 (Email: 'support@dataflex.com', Address: '702 Pine Rd', Phone: '987-654-3210') in Zendesk. Specify what should be listed under conflicting fields. | pass→pass | 2,994 | 3,553 | +19% | 1 | 1 | 0% | 726 | 1,124 | +55% | 0 | 0 | — |
▸case-18 A script proposed setting Record B (Name: 'Horizon Labs', Phone: null, Domain: null) as the survivor over Record A (Name: 'Horizon Labs Inc', Phone: '555-9000', Domain: 'horizonlabs.io') during a database merge. Evaluate this proposal. | pass→pass | 10,048 | 4,520 | -55% | 1 | 1 | 0% | 1,604 | 1,236 | -23% | 0 | 0 | — |
▸case-19 Compare Prospect 1 (Name: 'Robert Vance', Email: 'rvance@vancerefrig.com', Role: 'CEO') and Prospect 2 (Name: 'Bob Vance', Email: 'rvance@vancerefrig.com', Role: 'Chief Executive Officer') in Apollo.io. Assess match confidence and review status. | fail→pass | 8,014 | 5,094 | -36% | 1 | 1 | 0% | 1,438 | 1,532 | +7% | 0 | 0 | — |
▸case-20 Two company profiles in Marketo match on domain fintech-flow.com. Record 1 has HQ: 'London', CRM Status: 'Customer', and ARR: '$500k'. Record 2 has HQ: 'Dublin', CRM Status: 'Prospect', and ARR: '$250k'. Itemize the conflicting details. | pass→pass | 2,484 | 4,365 | +76% | 1 | 1 | 0% | 544 | 1,204 | +121% | 0 | 0 | — |
▸case-21 Compare Company A ('Summit Ridge Media', City: 'Denver') and Company B ('Summit Ridge Marketing', City: 'Denver') in a contact list. Neither record contains an email address or domain key. Assess match confidence. | pass→pass | 8,524 | 5,721 | -33% | 1 | 1 | 0% | 1,419 | 1,379 | -3% | 0 | 0 | — |
▸case-22 Evaluate two enterprise customer records in NetSuite sharing identical tax identification numbers but conflicting primary contact names ('Jane Doe' vs 'John Smith'). What value should be populated in manual_review_status? | pass→pass | 9,298 | 4,877 | -48% | 1 | 1 | 0% | 1,500 | 1,285 | -14% | 0 | 0 | — |