extend
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
extend has 10 facts recorded in Dontopedia across 5 references, with 4 live disagreements.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (4)
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.
hasAbilityToHas Ability to(1)
- Checked in System Prompt
ex:checked-in-system-prompt
methodMethod(1)
- Extend Operation
ex:extend-operation
operationOperation(1)
- Sample Extension
sample-extension
usesUses(1)
- Vectorize in Batches
ex:vectorize_in_batches
Other facts (8)
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.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | List Method | [1] |
| Rdf:type | List Method | [2] |
| Rdf:type | List Method | [4] |
| Rdf:type | Method | [5] |
| Applied to | results | [1] |
| Applied to | Closest Synonyms | [2] |
| Method of | List | [2] |
| Method of | results | [3] |
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 (5)
ctx:claims/beam/26ad62c1-2fdd-407e-9506-5441cf238c57- full textbeam-chunktext/plain1 KB
doc:beam/26ad62c1-2fdd-407e-9506-5441cf238c57Show excerpt
Let's assume your evaluation pipeline involves processing large tensors using PyTorch. Here's an example of how you might optimize it: ```python import torch import tracemalloc # Start tracing memory allocation tracemalloc.start() def ev…
ctx:claims/beam/f0cc860e-7f75-4530-abef-84dc82b5e5ad- full textbeam-chunktext/plain1 KB
doc:beam/f0cc860e-7f75-4530-abef-84dc82b5e5adShow excerpt
term_embedding = get_contextual_embeddings(term) closest_synonyms = [] for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_context…
ctx:claims/beam/272c0d0a-4573-48c3-b0aa-0b08ac646db4ctx:claims/beam/64506b18-1246-48ee-8a13-99cd50bdde6fctx:claims/beam/2e9fecea-ca91-4203-b029-db5f820e044a
See also
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