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Phase 4 Chronological Relational Processing chronrel

elb-pr edited this page Apr 7, 2026 · 2 revisions

Phase 4 — Chronological & Relational Processing (chronrel)

Phase 4 is the synthesis stage of the investigative workflow where structured entities identified in previous phases are mapped to identify patterns, causality, and hidden networks. This phase transitions from data collection to active analysis by normalizing temporal data into a UTC backbone and constructing relational graphs to reveal organizational vulnerabilities and operational coordination.

Data Flow: Phase 3 to Phase 4

The process ingests POLE (Person, Object, Location, Event) entities and their associated relationships to produce three primary analytical outputs: the Master Chronology, the Network Edge List, and specialized Verification Reports.

graph TD
    subgraph "Phase 3: Collation (Input)"
        ER["Entity Register (POLE)"]
        SR["Source Register (Admiralty)"]
        TL["Task Log"]
    }

    subgraph "Phase 4: chronrel (Processing)"
        T9["Step 9: Temporal Normalization"]
        T10["Step 10: Relational Processing"]
        T11["Step 11: Content Verification"]
        
        T9 --> |"UTC Timestamps"| MC["Master Chronology"]
        T10 --> |"SNA Metrics"| EL["Edge List"]
        T11 --> |"Authentication"| VR["Verification Reports"]
    }

    subgraph "Phase 5: hypcog (Output)"
        ACH["ACH Matrix"]
    }

    MC --> ACH
    EL --> ACH
    VR --> ACH
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Step 9: Temporal Normalization & Chronological Analysis

The objective of Step 9 is to establish a mathematical temporal backbone for the investigation. This prevents sequencing errors in multi-jurisdictional cases by enforcing a strict UTC standard.

Master Chronology Construction (t9.1)

Analysts perform a systematic source sweep across the Entity Register, Task Logs, and Subject Profiles to extract all dated events.

  • UTC Normalization: All timestamps must be converted to UTC. Investigators are required to record the original timestamp, the timezone offset applied, and verify DST (Daylight Savings Time) transitions.
  • Fields: Every entry must include UTC Date/Time, Factual Event Description, POLE Entity References, Source ID, Admiralty Confidence, and Temporal Certainty.

Gap and Conflict Management (t9.2 - t9.3)

  • Temporal Gaps: Analysts must insert placeholder entries for periods where entity activity is unknown. Gaps are classified as Evidential Absence, Investigative Gaps (requiring more collection), or Obstructive Gaps (evidence destroyed/concealed).
  • Conflict Resolution: When sources disagree, system-generated logs (bank headers, CCTV) take precedence over human recollection. Higher Admiralty grades also dictate the preferred timestamp.

Step 10: Relational Processing & Network Visualization

Step 10 transforms the Entity Register into a mathematical graph to identify structural vulnerabilities such as hubs and chokepoints.

Edge List Generation (t10.1)

The investigation uses the analysis/network-architecture.md template to compile a tabular list of relationships.

  • Mandatory Fields: Source Entity, Target Entity, Relationship Type (controlled vocabulary), and Evidence Citation.
  • Visual Grammar: Confirmed relationships (Admiralty 1-2) use solid lines; unconfirmed or inferred relationships use dashed lines.

Quantitative Social Network Analysis (SNA) (t10.2)

Before visual layout, the network is analyzed using network_analyzer.py to calculate objective metrics:

Metric Description Investigative Significance
Degree Centrality Count of direct connections Identifies leadership or communication hubs skills/claude-sleuth/references/chronrel/t10.2.md:11.
Betweenness Centrality Frequency on shortest paths Identifies structural chokepoints and gatekeepers skills/claude-sleuth/references/chronrel/t10.2.md:13.
Closeness Centrality Average path length to others Identifies entities that disseminate info efficiently skills/claude-sleuth/references/chronrel/t10.2.md:15.

Two-Mode Analysis (t10.4)

This technique identifies hidden co-occurrences by mapping different entity types (e.g., Person-to-Event or Person-to-Location). Shared affiliations suggest operational trust or coordination even if no direct communication is documented.


Step 11: Specialized Spatial & Media Authentication

Step 11 grounds the digital investigation in physical reality through forensic verification and spatial analysis.

Content Verification (t11.1)

High-impact digital assets (photos/videos) undergo a multi-step authentication pipeline using content_archiver.py and geolocation.py:

  1. Reverse Image Search: To detect repurposed or out-of-context media skills/claude-sleuth/references/chronrel/t11.1.md:17.
  2. Geolocation: Matching visual cues against satellite imagery skills/claude-sleuth/references/chronrel/t11.1.md:18.
  3. Chronolocation: Shadow analysis (SunCalc) and weather cross-referencing to estimate capture time skills/claude-sleuth/references/chronrel/t11.1.md:19.
  4. Manipulation Detection: Error Level Analysis (ELA) to find compression inconsistencies.

Map Regression & Spatial Econometrics (t11.2 - t11.3)

  • Map Regression: Systematic comparison of historical maps and satellite imagery to track physical development, land use changes, and building footprint alterations.
  • Spatial Econometrics: Analyzing the economic plausibility of a site. If a business claims revenue orders of magnitude higher than regional norms for its location, it is flagged as a financial anomaly (potential money laundering).

Implementation Mapping: Code to Intelligence Space

The following diagram bridges the technical script execution with the intelligence artifacts produced in Phase 4.

graph LR
    subgraph "Code Entity Space (Scripts)"
        NA["network_analyzer.py"]
        GL["geolocation.py"]
        CA["content_archiver.py"]
    end

    subgraph "Analytical Space (Templates)"
        NA --> |"generates"| T_NA["analysis/network-architecture.md"]
        GL --> |"populates"| T_VER["analysis/verification.md"]
        CA --> |"populates"| T_MOR["analysis/morphological.md"]
        T_CM["analysis/chronological-matrix.md"]
    end

    subgraph "Intelligence Space (Concepts)"
        T_NA --> |"defines"| Hubs["Central Hubs & Bridges"]
        T_CM --> |"defines"| UTC["UTC Master Chronology"]
        T_VER --> |"defines"| Authenticity["Media Provenance"]
        T_MOR --> |"defines"| Plausibility["Economic Plausibility"]
    end
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Phase 4 Quality Checkpoints

To maintain rigor, the following standards are enforced:


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