Three-Claim Trace - finding -> tool execution -> evidence

This walks three approved findings from the current Mr. Evil VPS showcase bundle end to end, using only committed files in showcase_runs/mr_evil_openai_gpt_5_2_resume/.

The snapshot status is ERROR, but the exported report frontmatter is REVIEWED because seven findings were examiner-approved before export. The bundle is also labeled as a GPT-5.2 resume snapshot, not a clean model benchmark: status.txt reports openai-chat:gpt-5.2, while earlier audit rows still show openai-chat:gpt-5-mini from the already-running case.

Every reviewed finding traces through this chain:

report.md finding F-NNN
  -> approved finding record in findings.json
    -> observation_id O-NNN and interpretation_id I-NNN in findings.json
      -> cited record_id + verbatim span_text
        -> audit/findings.jsonl record_observation / record_interpretation rows
          -> audit/index.jsonl search_evidence / get_record rows that surfaced the record
            -> audit/hypothesis.jsonl confirm/pivot row tying evidence to the hypothesis

findings.json intentionally contains multiple object types. An approved finding record is compact and points to an observation and interpretation; the observation object contains the text and cited spans.

Claim 1 - F-002: Exported-PST folder shortcut

• Report line: report.md lists F-002 as a HIGH-confidence finding.
• Approved finding record: findings.json maps F-002 to observation_id: O-033 and interpretation_id: I-002.
• Observation: O-033 records LNK Recent shortcut: Exported-PST.lnk with cited record_id 2507943.
• Cited span: LNK target=G:\My Drive\STARK-RESEARCH-LABS FOLDER\Exported-PST.
• Tool execution: audit/findings.jsonl contains record_observation for O-033 and record_interpretation for I-002; audit/index.jsonl contains get_record rows for record_id 2507943.
• Critic: audit/critic.jsonl records AGREE verdicts for O-033 and F-002.

Claim 2 - F-005: SRL-EMAIL-EXPORT.pst path

• Report line: report.md lists F-005 as a MEDIUM-confidence finding.
• Approved finding record: findings.json maps F-005 to observation_id: O-047 and interpretation_id: I-005.
• Observation: O-047 records the PST shortcut content with cited record_id 2508004.
• Cited span: LNK target=G:\My Drive\STARK-RESEARCH-LABS FOLDER\Exported-PST\SRL-EMAIL-EXPORT.pst workdir=G:\My Drive\STARK-RESEARCH-LABS FOLDER\Exported-PST.
• Tool execution: audit/findings.jsonl contains record_observation for O-047 and record_interpretation for I-005; audit/index.jsonl and audit/agent.jsonl contain get_record / confirm_hypothesis activity for record_id 2508004.
• Critic: audit/critic.jsonl includes multiple AGREE verdicts and several REJECT verdicts where the critic pushed back on unsupported "fredr's Recent folder" wording. The approved report keeps the cautious interpretation: the LNK proves the PST path was referenced, not that the user manually uploaded it.

Claim 3 - F-006: Google DriveFS execution

• Report line: report.md lists F-006 as a HIGH-confidence finding.
• Approved finding record: findings.json maps F-006 to observation_id: O-048 and interpretation_id: I-006.
• Observation: O-048 records a Google DriveFS prefetch entry with cited record_id 2505148.
• Cited span: Prefetch exe=GOOGLEDRIVEFS.EXE run_count=22.
• Tool execution: audit/findings.jsonl contains record_observation for O-048 and record_interpretation for I-006; audit/index.jsonl and audit/agent.jsonl contain search_evidence / get_record activity around GOOGLEDRIVEFS and record_id 2505148.
• Critic: audit/critic.jsonl agrees that the prefetch proves DriveFS execution, while some challenge rows correctly note that drive-letter mapping would need additional MountPoints or registry evidence.

Reproduce the trace

RUN=docs/showcase_runs/mr_evil_openai_gpt_5_2_resume

# Find the approved report sections.
grep -n "F-002\\|F-005\\|F-006" "$RUN/report.md"

# Show the finding, observation, and interpretation records without relying on line-wrapped JSON.
python - <<'PY'
import json
from pathlib import Path

run = Path("docs/showcase_runs/mr_evil_openai_gpt_5_2_resume")
records = json.loads((run / "findings.json").read_text())

for finding_id in ("F-002", "F-005", "F-006"):
    finding = next(r for r in records if r.get("finding_id") == finding_id)
    obs = next(r for r in records if r.get("observation_id") == finding["observation_id"] and "text" in r)
    interp = next(i for i in obs.get("interpretations", []) if i["interpretation_id"] == finding["interpretation_id"])
    print(finding_id, finding["observation_id"], finding["interpretation_id"])
    print(obs["text"])
    print(obs["cited_spans"])
    print(interp["text"])
    print()
PY

# Confirm the exact audit rows that produced or criticized those records.
grep -n "O-033\\|I-002\\|F-002\\|2507943" "$RUN/audit/"*.jsonl
grep -n "O-047\\|I-005\\|F-005\\|2508004" "$RUN/audit/"*.jsonl
grep -n "O-048\\|I-006\\|F-006\\|2505148" "$RUN/audit/"*.jsonl