for tool in tools
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-05.)
for tool in tools has 13 facts recorded in Dontopedia across 3 references, with 1 live disagreement.
Mostly:rdf:type(3), iterates over(2), body contains(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound 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.
containsLoopContains Loop(1)
- Script
ex:script
followsFollows(1)
- Threshold Check
ex:threshold-check
hasLoopHas Loop(1)
- Implementation
ex:implementation
iteratedByIterated by(1)
- Tool Collection
ex:tool-collection
nestedWithinNested Within(1)
- Document Loop
ex:document-loop
occursInsideOccurs Inside(1)
- Recall Score Calculation
ex:recall-score-calculation
thirdStepThird Step(1)
- Code Sequence
ex:code-sequence
Other facts (12)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
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.
References (3)
ctx:claims/beam/5e4120cd-154f-4526-806b-66e6ad6a75b5- full textbeam-chunktext/plain1 KB
doc:beam/5e4120cd-154f-4526-806b-66e6ad6a75b5Show excerpt
[Turn 1166] User: I'm working on a proof of concept for testing 2 retrieval tools on 400 documents, and I want to achieve 90% recall, but I'm having trouble with the implementation, can someone help me with this? ```python import numpy as …
ctx:claims/beam/eb7f55ff-6715-4dd8-81f8-023b5f9693f2- full textbeam-chunktext/plain1 KB
doc:beam/eb7f55ff-6715-4dd8-81f8-023b5f9693f2Show excerpt
retrieved_labels = relevant_labels[retrieved_indices] true_positives = np.sum(retrieved_labels) recall = true_positives / num_relevant return recall # Initialize the recall scores recall_scores = [] for tool in tools: …
ctx:claims/beam/697d8ceb-4767-4332-ba36-3922b2447184- full textbeam-chunktext/plain1 KB
doc:beam/697d8ceb-4767-4332-ba36-3922b2447184Show excerpt
import random # Define the retrieval tools tools = ['tool1', 'tool2'] # Define the documents documents = [f'document{i}' for i in range(400)] # Define the evaluation metrics metrics = ['recall', 'precision', 'f1_score'] # Initialize the…
See also
Keep researching
Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.