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How do I use a schema markup generator tool to implement custom organization and author schema for E-E-A-T compliance?

ChatGPTgpt-62 queries11/1/2026
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Fan-out diagram

The searches the AI actually ran for this prompt.

SEED QUERY
How do I use a schema markup generator tool to implement custom organization and author schema for E-E-A-T compliance?
2 real searches · 1 real-search pass
Google Search Central organization structured data documentation author article profile page structured data E-E-A-T schema markupGoogle ↗
OBSERVEDOther / navigational
Merkle schema markup generator organization person JSON-LD toolGoogle ↗
OBSERVEDComparison

Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.

Query classes

Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.

ChatGPT real searches (2)
Short factOther / navigational50%Comparison50%ConsequenceReasonDefinitionInstructionYes / no

Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.

Searches and sources

ChatGPT2 real searches · gpt-6
Google Search Central organization structured data documentation author article profile page structured data E-E-A-T schema markup Google ↗
Merkle schema markup generator organization person JSON-LD tool Google ↗

Queries

2 queries · click a column to sort
Query / typeSearchIntentQuery classRouting formatClusterFreq ↓Weight
Google Search Central organization structured data documentation author article profile page structured data E-E-A-T schema markup
OBSERVED
Google ↗
AI Mode ↗
—Other / navigational——1x
of 1
Merkle schema markup generator organization person JSON-LD tool
OBSERVED
Google ↗
AI Mode ↗
—Comparison——1x
of 1