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docs: correct credibility-weighted example output values

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Fix stale/incorrect weight and confidence figures in the conflict
resolution guide that don't match actual resolver output, and update
a leftover credibility_score field reference in Common Pitfalls.
This commit is contained in:
KaifAhmad1
2026-07-07 16:03:19 +05:30
parent 8d60f68fcd
commit 6bfb9c719c
1 changed files with 4 additions and 4 deletions
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docs/guides/conflict-resolution.md
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@@ -297,7 +297,7 @@ for r in results:
Strategy used : credibility_weighted
Confidence : 36%
Sources used : ['nvd', 'commercial_feed', 'vendor_paloalto']
Notes : Resolved by credibility-weighted voting (weight: 0.49)
Notes : Resolved by credibility-weighted voting (weight: 0.98)
[RESOLVED] cve-2024-3400_exploit_status_conflict
Resolved value : in_wild
@@ -400,7 +400,7 @@ for conflict, result in zip(all_conflicts, results):
```
```text
cvss_score = 10.0 (from ['nvd'], confidence 72%)
cvss_score = 10.0 (from ['nvd', 'commercial_feed', 'vendor_paloalto'], confidence 36%)
exploit_status = in_wild (from ['commercial_feed'], confidence 80%)
```
@@ -502,7 +502,7 @@ for r in results:
print(f"{r.conflict_id}: {r.resolved_value!r} [{r.confidence:.0%} confidence]")
# apt29_nation_state_conflict: 'Russia' [86% confidence]
# apt29_first_seen_conflict: '2008' [44% confidence]
# The blog's China attribution (weight 0.15) loses to Mandiant+CrowdStrike (0.475+0.46).
# The blog's China attribution (weight 0.30) loses to Mandiant+CrowdStrike (0.95+0.92).
history = resolver.get_resolution_history()
print(f"Audit log entries: {len(history)}")
@@ -683,7 +683,7 @@ If duplicate nodes for the same real-world entity still exist, `ConflictDetector
Calling `detect_value_conflicts()` for every property in a manual loop produces redundant passes over your data. Use `detect_entity_conflicts()` instead — it handles all properties in a single call and is the recommended starting point for bulk detection.
**Misunderstanding credibility scores**
Credibility scores are weights you assign based on your prior knowledge of source reliability — not ground truth. A source with `credibility_score: 0.99` can still be wrong. `CREDIBILITY_WEIGHTED` resolution amplifies your beliefs about source quality; if those beliefs are miscalibrated, the resolutions will be too. Validate scores against known ground truth before relying on them in production.
Credibility scores are weights you assign based on your prior knowledge of source reliability — not ground truth. A source registered with `set_source_credibility("source", 0.99)` can still be wrong. `CREDIBILITY_WEIGHTED` resolution amplifies your beliefs about source quality; if those beliefs are miscalibrated, the resolutions will be too. Validate scores against known ground truth before relying on them in production.
**Treating resolved values as guaranteed truth**
A resolved value is the most defensible answer given your sources and strategy — not necessarily the correct one. Low confidence scores and `EXPERT_REVIEW` flags are signals to scrutinize results before writing them to a canonical record or downstream system.