ai-knowledge-graph

Verified AI Infrastructure

The Verified AI Knowledge Graph

Resolution Assurance is building a trust-first knowledge graph where every entity, edge, claim, and training example can be traced back to sealed evidence, transparency logs, validator attestations, and machine-readable provenance.

Evidence-linked entities Validator-attested graph trust AI training data generation
Layer 1
Verified Data Ingestion
Public signals, structured evidence, and append-only proof records enter the system with quality scoring and provenance controls.
Layer 2
Logical Knowledge Graph
Entities, predicates, edges, documents, evidence, and claims are compiled into a trust-weighted graph foundation.
Layer 3
Model Training Readiness
Graph-derived examples can be exported into retrieval, reasoning, and training pipelines for next-generation AI systems.
Why This Matters

AI needs a source of truth, not just a larger pile of text

Most AI systems are trained on data that is large, fast, and cheap, but not reliably grounded. Resolution Assurance is building a verified knowledge layer where the graph can be traced back to sealed records, evidence links, validator attestations, and measurable trust scores.

This page shows the transition from proof infrastructure into a machine-readable knowledge graph and then into training-ready datasets for retrieval, reasoning, and recommendation systems.

Verified Inputs
Evidence before inference
Records are ingested, quality-scored, sealed, witnessed, and rooted before they are elevated into trusted graph material.
Structured Knowledge
Entities, edges, claims
The system compiles evidence-linked entities, predicates, claims, and relationships into a reusable graph foundation.
Training Readiness
Datasets with provenance
Graph outputs can be exported into AI training and retrieval systems with provenance, trust signals, and validation context preserved.
RSE

The Reality Synthesis Engine is translating all system signals into a single interpretable outcome.

Reality Synthesis Engine | Score: 42.4

Score
42.4
Signal Type
STABLE
Updated
2026-04-14T05:05:09+00:00
PEWS

The Personal Early Warning System is calibrating all domain signals through confidence stability and priority locking.

Personal Early Warning System | Score: 30.1

Score
30.1
State
WATCH
Updated
2026-04-14T05:05:09+00:00
ARHS

The AI-Reality-Human-Synthetic Engine is mapping the divergence between artificial intelligence outputs and verified human reality.

AI-Reality-Human-Synthetic Engine | Score: 16.4

Score
16.4
AR State
ALIGNED
Updated
2026-04-14T05:05:09+00:00
AM

Assurance Model is online.

Assurance Model | Score: 40.7

Score
40.7
State
EARLY
Updated
2026-04-14T05:10:07+00:00
RI

Risk Instruments is online.

Risk Instruments | Score: 83.3

Score
83.3
State
STRONG
Updated
2026-04-14T05:10:07+00:00
LFTE

Learning & Feedback Truth Engine is online.

Learning & Feedback Truth Engine | Score: 95.5

Score
95.5
State
CONFIRMED
Updated
2026-04-14T05:05:07+00:00
Artefact Registry
14,673
585 sealed / 14088 observed / 583 rooted
Signal Layer
14,673
Signals observed 14673 · verified 585 · avg verified trust 0.00
Root Validator
rr_20260414_050531_63919
81 new leaves / log size 63919 / partial_attestation
1 validator attestations / target 2
Node Network
3/3
Validator quorum established
Validators online 3/3 · quorum target 2
RAPID Ingestion

RAPID ingestion layer is active with indexed identifiers, resolver cache entries, and live resolution telemetry.

This layer indexes public source records into open-generated RAPID IDs, raw ingestion records, identifier registry rows, resolver cache records, and resolution telemetry.

Sources
32/49
Raw Records
24,536
Open RAPID IDs
25,360
Cache Entries
25,360
Indexed records 24,536 Resolution events 15 Avg latency 1.0 ms rapid:{object_type}:{ulid}
RAPID Layer

RAPID Resolution Layer

RAPID provides persistent identifiers and resolver endpoints for proofs, artifacts, claims, certificates, and validator records across the Resolution Assurance platform.

Resolver Status
Federated resolver ready
Namespaces 8
Namespaces
8
Indexed Proofs
40,024
Resolver Nodes
3/3
Snapshot Version
7,272
Resolve API /wp-json/ra/v2/rapid/resolve/{rapid_id} Graph API /wp-json/ra/v2/rapid/graph/{rapid_id}
Knowledge Graph

Verified Knowledge Graph

Verified records are compiled into entities, predicates, edges, evidence links, claim records, and training-ready graph structures.

Graph Status
Operational
Structured registry and export layer
Entities
40,024
Predicates
8
Edges
65,210
Claim Records
200
Evidence 1,141 Avg entity trust 0.86 Fact candidates 65,062 Approved claims 148
Global Evidence Graph

Provenance-Linked Evidence Graph

Evidence graph is online with triangulated claims, source provenance, and temporal claim coverage.

Triangulated Claims
51
Contested Claims
0
Temporal Claims
200
Source Registry
19
Evidence records 1,141 Root-anchored facts 0 Avg source weight 0.71 Api Feed 19
Claims Layer

Evidence-Linked Claim Review

Claims are generated from verified records, then approved before they are promoted into higher-confidence graph and training outputs.

Active Review Queue
200
Approved for Graph Promotion
148
Pending
52
Avg Approved Trust
0.99
Auto-approved 148 Policy Enabled Trust threshold 0.97 Quality threshold 95.0
AOE

The Authenticity Origin Engine is classifying signal origin using behavioral triangulation — timing variance, language entropy, and coordination patterns.

Authenticity Origin Engine | Score: 93.5

Score
93.5
Origin Label
Predominantly Human
Updated
2026-04-14T05:05:08+00:00
NSDE

The Negative Space Detection Engine is identifying what should exist in reality but is absent — detecting silent failures and emerging gaps before they surface as crises.

Negative Space Detection Engine | Score: 17.1

Score
17.1
Gap Type
No Significant Gap
Updated
2026-04-14T05:05:08+00:00
LTE

The Latency of Truth Engine is measuring how long accurate information takes to propagate and displace false signals across the reality layer.

Latency of Truth Engine | Score: 0.4

Score
0.4
Latency State
MINIMAL
Updated
2026-04-14T05:05:09+00:00
TRI

TRI — the Triangulated Reality Index — unifies state, inevitability, early detection, and pressure into a single master truth object answering: what is happening, why, how early, and what happens next.

Triangulated Reality Index | Score: 91.4

Score
91.4
Stage
Possible
Updated
2026-04-14T05:05:08+00:00
RPH

The Reality Phase Engine is classifying the current state of reality — Stable, Building Pressure, Accelerating, Fragile, Breaking, or Recovering — as the system's human-readable output layer.

Reality Phase Engine | Score: 0.1

Score
0.1
Phase
Recovering
Updated
2026-04-14T05:05:08+00:00
RIE

The Reality Inevitability Engine is measuring the transition from possible → probable → inevitable — detecting the point of no return before mainstream systems see it.

Reality Inevitability Engine | Score: 0.3

Score
0.3
Stage
Possible
Updated
2026-04-14T05:05:08+00:00
RTI

The Reality Trajectory Index is computing where reality is heading and how fast — combining inevitability, momentum, cross-domain alignment, and latency advantage.

Reality Trajectory Index | Score: 11.6

Score
11.6
Direction
Stable
Updated
2026-04-14T05:05:08+00:00
RDIE

The Reality Drift Intelligence Engine is the early warning core — unifying current state, drift, and latency advantage into a single Reality Break Risk score.

Reality Drift Intelligence Engine | Score: 4.3

Score
4.3
Drift State
Stable
Updated
2026-04-14T05:05:08+00:00
RCIE

The RSI Causal Intelligence Engine is mapping causal chains driving reality instability — identifying root causes, cascade triggers, and temporal alignment of signal pressures.

RSI Causal Intelligence Engine | Score: 2.4

Score
2.4
Causal State
STABLE_CAUSE
Updated
2026-04-14T05:05:09+00:00
BAE

The Behavior and Attention Engine is tracking cognitive drift, distraction signals, and attention fragmentation across the human-information interface.

Behavior & Attention Engine | Score: 0.2

Score
0.2
Attention
FOCUSED
Updated
2026-04-14T05:05:09+00:00
AIRI

The AI Reality Impact Index is measuring the real-world disruption signature of artificial intelligence across labour, narrative, and adoption vectors.

AI Reality Impact Index | Score: 0.8

Score
0.8
Impact State
LOW_IMPACT
Updated
2026-04-14T05:05:09+00:00
Model Training

Graph-derived training data is ready for retrieval and reasoning workflows.

Exports are staged for LLM pretraining, retrieval-augmented generation, reasoning models, and recommendation systems using graph-derived examples.

Training Examples
65,995
Retrieval ready Yes Reasoning ready Yes Formats PARQUET, JSONL, TFRECORD Frameworks pytorch, jax, deepspeed
Dataset Catalog

Training Dataset Manifest

Enterprise datasets are packaged as a paid graph-training catalog with controlled access, row-count estimates, and downstream purpose metadata.

Access Tier
Paid and Enterprise
Manifest 1.0
knowledge_graph_entities
entities.csv
Canonical entity registry for graph ingestion and retrieval pipelines.
Format CSV,JSON Rows 40,018
knowledge_graph_edges
edges.csv
Evidence-linked graph edges for traversal and reasoning workloads.
Format CSV,JSON Rows 65,210
edge_evidence
edge_evidence.csv
Per-edge evidence attachments and extraction metadata for auditability.
Format CSV,JSON Rows 65,062
knowledge_graph_claims
claims.csv
Reviewed and approved claims for supervised validation workflows.
Format CSV,JSON Rows 148
training_examples
training_examples.jsonl
Retrieval and claim validation examples for model training and evaluation.
Format JSONL,JSON Rows 65,995
Schema Exports
Paid plan required

Download Graph and Training Data Structures

These exports are the first machine-readable outputs of the verified knowledge graph layer. Each dataset can be consumed by downstream graph engines, retrieval systems, or AI training pipelines.

Public summary exports
Open access summaries for the graph, claim, and ontology layers.
Deep dataset exports are paid-only
Entities, edges, claims, documents, and training_examples.jsonl require a paid plan.
Upgrade Plan
Intelligence Layer

Operational Intelligence Summary

Registry quality is strong under RAS-100, with active transparency publication and verified signal scoring.

Generated
2026-04-14T05:17:15+00:00
Total corpus 24,536 Recent signal sample 60 Sample observed_record: 60 Sample sealed_proof: 0 Sample rooted_proof: 0 RAS-100 94 / Exceptional Remediation 0