Structured Data Extraction inside the LeadsLogix engine
Understand exactly how LeadsLogix harvest JSON-LD, microdata, and schema.org entities as the highest-confidence extraction source — then put the same engine to work on your data.
This is a deep dive into the structured data extraction — the part of the LeadsLogix platform built to harvest JSON-LD, microdata, and schema.org entities as the highest-confidence extraction source. It covers JSON-LD parsing, microdata extraction, and Person/Organization entity mapping, and how the subsystem's output feeds the rest of the pipeline.
5+
Entity types mapped
The defining number behind structured data extraction inside the LeadsLogix engine.
5
Extraction layers
This subsystem operates inside the 5-layer scraping hierarchy with strict per-company budgets.
Structured Data Extraction workspace
Live pipeline console
5+
Entity types mapped
The defining number behind structured data extraction inside the LeadsLogix engine.
5
Extraction layers
This subsystem operates inside the 5-layer scraping hierarchy with strict per-company budgets.
0-100
Confidence scoring
Outputs carry confidence scores so downstream stages know exactly how much to trust them.
Audit
Source lineage
Every fact this subsystem produces keeps its source URL and timestamp attached.
Subsystem health
98%
Live status for structured data extraction: throughput, error rates, and budget consumption.
Output quality
86%
Confidence distributions and review queues for everything this subsystem produced, focused on JSON-LD parsing, microdata extraction, and Person/Organization entity mapping.
Source coverage
74%
Which of JSON-LD blocks, microdata attributes, schema.org types, and OpenGraph metadata contributed results, and where coverage gaps remain.
Run history
62%
Per-run timings, escalations, and outcomes so behavior changes are visible across runs.
Structured Data Extraction run preview
Representative LeadsLogix workspace module for pipeline, verification, enrichment, or analytics views.
Real subsystem, real code
This page documents structured data extraction as it actually runs in the LeadsLogix pipeline — JSON-LD parsing, microdata extraction, and Person/Organization entity mapping.
Source-backed output
Everything it produces stays tied to JSON-LD blocks, microdata attributes, schema.org types, and OpenGraph metadata, with evidence preserved on the record.
Budgeted and bounded
Page, render, and runtime budgets bound this subsystem, so cost and behavior stay predictable at any scale.
Composable by design
It exposes its results to the orchestrators, the intelligence graph, and the export pipeline through stable contracts.
Architecture proof
Structured Data Extraction is backed by the LeadsLogix engine
Every page in this cluster points to a real product capability: discovery, scraping, enrichment, verification, cleanup, scoring, merge, and CRM export.
Self-declared data first
Schema.org Person and Organization entities are the site's own structured claims — the most reliable extraction source on any page.
Full vocabulary mapping
Person, Organization, ContactPoint, PostalAddress, and JobPosting entities map directly onto pipeline record fields.
Malformed-markup tolerance
Broken JSON-LD, truncated blocks, and nonstandard nesting are repaired or partially recovered instead of discarded.
Platform architecture
Workflow for harvest JSON-LD, microdata, and schema.org entities as the highest-confidence extraction source
The page is structured as a working SaaS workflow for engineers harvesting machine-readable page data, with each step connected to the local LeadsLogix pipeline.
Receive scoped work
The orchestrator hands this subsystem its inputs with budgets and confidence targets already attached.
Execute against sources
It works JSON-LD blocks, microdata attributes, schema.org types, and OpenGraph metadata to harvest JSON-LD, microdata, and schema.org entities as the highest-confidence extraction source.
Score the results
Outputs are scored for confidence so the escalation and validation layers can act on them mechanically.
Persist the evidence
Findings land in the intelligence graph with source URLs, timestamps, and confidence attached.
Feed the next stage
Downstream stages — enrichment, verification, scoring, export — consume the results through stable contracts.
Dashboard UX
Console-first pages for enterprise buyers
Each page uses the same product-console pattern: source mapping, pipeline health, quality review, and export packaging. It feels like a SaaS system because the content mirrors how LeadsLogix actually runs data jobs.
Subsystem health
Live status for structured data extraction: throughput, error rates, and budget consumption.
Output quality
Confidence distributions and review queues for everything this subsystem produced, focused on JSON-LD parsing, microdata extraction, and Person/Organization entity mapping.
Source coverage
Which of JSON-LD blocks, microdata attributes, schema.org types, and OpenGraph metadata contributed results, and where coverage gaps remain.
Run history
Per-run timings, escalations, and outcomes so behavior changes are visible across runs.
Structured Data Extraction workspace
Live pipeline console
5+
Entity types mapped
The defining number behind structured data extraction inside the LeadsLogix engine.
5
Extraction layers
This subsystem operates inside the 5-layer scraping hierarchy with strict per-company budgets.
0-100
Confidence scoring
Outputs carry confidence scores so downstream stages know exactly how much to trust them.
Audit
Source lineage
Every fact this subsystem produces keeps its source URL and timestamp attached.
Subsystem health
98%
Live status for structured data extraction: throughput, error rates, and budget consumption.
Output quality
86%
Confidence distributions and review queues for everything this subsystem produced, focused on JSON-LD parsing, microdata extraction, and Person/Organization entity mapping.
Source coverage
74%
Which of JSON-LD blocks, microdata attributes, schema.org types, and OpenGraph metadata contributed results, and where coverage gaps remain.
Run history
62%
Per-run timings, escalations, and outcomes so behavior changes are visible across runs.
Use cases
Structured Data Extraction use cases
Focused entry points for engineers harvesting machine-readable page data who need source-backed lead generation, database enrichment, and verified contacts.
Harvest JSON-LD
Use LeadsLogix to move this workflow from manual research into repeatable discovery, verification, scoring, and export.
Map schema entities
Use LeadsLogix to move this workflow from manual research into repeatable discovery, verification, scoring, and export.
Recover broken markup
Use LeadsLogix to move this workflow from manual research into repeatable discovery, verification, scoring, and export.
Source focus
JSON-LD blocks, microdata attributes, schema.org types, and OpenGraph metadata
Proof focus
JSON-LD parsing, microdata extraction, and Person/Organization entity mapping
Output focus
CRM-ready Excel and CSV records with company, contact, domain, verification, source, confidence, and audit fields.
Structured Data Extraction questions
Short answers for buyers reviewing the product, service, platform, or industry workflow.
Still have questions?
Our team can walk you through the pipeline, pricing, and your use case.
Continue through the LeadsLogix architecture
Related product, service, platform, and industry pages for the same workflow family.
Next action
Build this page cluster into a working acquisition path
Start with the highest-intent records, attach proof from the pipeline, and route visitors to CSV upload, workspace registration, or a managed delivery call.