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Intent Accuracy

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

Intent Accuracy has 5 facts recorded in Dontopedia across 3 references, with 1 live disagreement.

5 facts·3 predicates·3 sources·1 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

  • Metric[1]all time · 14d0c405 2f52 4261 Ad38 13be7b76835d
  • Metric[2]sourceall time · 5d5ac388 Fe7b 46be 8676 6c933e883590
  • Metric Type[3]all time · 63f3f6ff B059 492e 954d Ccca67c2349d

Improved byimprovedBy

Rdfs:labelrdfs:label

  • Intent accuracy[1]all time · 14d0c405 2f52 4261 Ad38 13be7b76835d

Inbound mentions (7)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

accuracyMetricAccuracy Metric(1)

accuracyTypeAccuracy Type(1)

appliesToApplies to(1)

hasPerformanceMetricHas Performance Metric(1)

improvesImproves(1)

measuresMeasures(1)

targetMetricTarget Metric(1)

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

improvedBybeam/14d0c405-2f52-4261-ad38-13be7b76835d
ex:llm-based-reformulation
labelbeam/14d0c405-2f52-4261-ad38-13be7b76835d
Intent accuracy
typebeam/14d0c405-2f52-4261-ad38-13be7b76835d
ex:Metric
typebeam/5d5ac388-fe7b-46be-8676-6c933e883590
ex:Metric
typebeam/63f3f6ff-b059-492e-954d-ccca67c2349d
ex:Metric-type

References (3)

3 references
  1. customctx:claims/beam/14d0c405-2f52-4261-ad38-13be7b76835d
  2. [2]beam-chunk1 fact
    customctx:claims/beam/5d5ac388-fe7b-46be-8676-6c933e883590
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5d5ac388-fe7b-46be-8676-6c933e883590
      Show excerpt
      [Turn 10558] User: I'm conducting a POC to test LLM reformulation on 1,500 queries, and I'm hitting 91% intent accuracy. However, I'm not sure how to optimize my model for better performance. Can you help me explore different algorithms and
  3. [3]beam-chunk1 fact
    customctx:claims/beam/63f3f6ff-b059-492e-954d-ccca67c2349d
    • full textbeam-chunk
      text/plain1020 Bdoc:beam/63f3f6ff-b059-492e-954d-ccca67c2349d
      Show excerpt
      However, I'm only achieving about 80% accuracy with this approach. I've studied LLM-based reformulation and noted a 25% intent accuracy boost for 6,000 complex queries. Can you help me improve my implementation to reach at least 92% detecti

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