The Scenario
You inherited an Excel workbook from the analyst who left three weeks ago. Column A of the 'News Snippets' sheet has 200 news article snippets she'd been collecting for a market intelligence project — tech company names, executive quotes, product announcements, all of it unstructured. The brief you received says the next step is to build a structured entity database: which companies appear, which executives, which locations. The workbook is supposed to feed a dashboard by end of week.
The bad version:
- Copy the first snippet from column A, paste it into the TextRazor API explorer, scroll through the JSON response looking for the entity array, manually note the names and types, paste them into columns B and C, move to row 2.
- Repeat for 200 rows, making judgment calls each time about which entities matter and whether a relevance score of 0.4 is worth including.
- Realize at row 40 that you haven't been consistent about how you're formatting the type field — "ORG" vs "organization" vs "Company" — and now you have to decide whether to go back and fix rows 1–39 or live with the inconsistency.
NLP output specifically punishes this approach because the interesting signal is in the aggregate. One entity mention tells you nothing. Two hundred of them, sorted by frequency across documents, starts telling you something. Manual copy-paste gives you nothing you can aggregate because the output never lands in a consistent format.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads the workbook, understands the column structure, and through its built-in TextRazor integration can run entity extraction across every row and write structured results back into the columns you specify.
For each text in column A of the 'News Snippets' sheet, use TextRazor entity extraction and write the extracted entity names and types (person/organization/place) into columns B and C, one cell per row.
What You Get
- Column B receives a comma-separated list of the top entities extracted from each row's text, ranked by relevance score.
- Column C receives the corresponding type for each entity — person, organization, or place — in the same order.
- Rows where TextRazor returns no high-confidence entities get a note in column B rather than a silent empty cell.
- The output format is consistent across all 200 rows — same delimiter, same type vocabulary, no judgment calls per row.
What If the Data Is Not Quite Ready
The snippets include HTML tags from a scrape
For each text in column A of the 'News Snippets' sheet, strip any HTML tags before sending to TextRazor entity extraction, then write the extracted entity names and types into columns B and C.
Some rows are blank or contain only URLs
For each text in column A of the 'News Snippets' sheet, skip blank rows and rows that contain only a URL, run TextRazor entity extraction on the remaining rows, and write entity names and types into columns B and C. Leave B and C empty for skipped rows.
You need entities from two worksheets combined
Combine the texts from column A of the 'Q1 News' sheet and column A of the 'Q2 News' sheet, run TextRazor entity extraction on each, and write entity names and types into columns B and C of each respective sheet.
Full kill chain: clean, extract, frequency count
For each text in column A of the 'News Snippets' sheet, remove HTML tags and trim whitespace, run TextRazor entity extraction, write entity names and types into columns B and C, and then add a 'Top Entities' summary sheet showing each unique entity name, its type, and how many rows it appeared in — sorted by frequency descending.
Handling cleanup and extraction in the same ask means the output is ready to aggregate without a separate formatting pass.
Try It
Get the 7-day free trial of SheetXAI and open any Excel workbook where a column holds a batch of text snippets waiting to be structured. Ask SheetXAI to run TextRazor entity extraction and write the results into the adjacent columns. Check out the IAB classification spoke if your next step is content categorization by taxonomy.