sad
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
sad has 21 facts recorded in Dontopedia across 9 references, with 4 live disagreements.
21 facts·7 predicates·9 sources·4 in dispute
Mostly:rdf:type(9), has synonym(4), semantic category(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (21)
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.
containsContains(6)
- Terms
ex:terms - Terms
ex:terms - Test Terms
ex:test-terms - Test Terms List
ex:test-terms-list - Words Array
ex:words-array - Words Variable
ex:words-variable
appearedAppeared(1)
- Little Girl Shelter
ex:little-girl-shelter
characterizedExperienceAsCharacterized Experience As(1)
- Andrew
ex:andrew
containsElementContains Element(1)
- Words List
ex:words-list
containsEmotionTermContains Emotion Term(1)
- Words List
ex:words-list
containsExactContains Exact(1)
- Words Array
ex:words-array
describedAsByDescribed As by(1)
- State of Education
ex:state-of-education
describesExperienceAsDescribes Experience As(1)
- Andrew
andrew
elementElement(1)
- Test Terms
ex:test-terms
expressedEmotionExpressed Emotion(1)
- Maria
ex:maria
hasEmotionSystemHas Emotion System(1)
- 3dchat
ex:3dchat
processesTermsProcesses Terms(1)
- Code Snippet
ex:code-snippet
Other facts (19)
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.
19 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | String | [1] |
| Rdf:type | Word | [2] |
| Rdf:type | Emotion | [3] |
| Rdf:type | Word | [4] |
| Rdf:type | String | [5] |
| Rdf:type | Term | [6] |
| Rdf:type | String Literal | [7] |
| Rdf:type | Test Term | [8] |
| Rdf:type | Emotion | [9] |
| Has Synonym | Unhappy | [1] |
| Has Synonym | Depressed | [1] |
| Has Synonym | Unhappy | [2] |
| Has Synonym | Depressed | [2] |
| Semantic Category | Emotion | [6] |
| Semantic Category | Negative Emotion | [7] |
| Synonym Count | 2 | [2] |
| Is Member of | Words Variable | [5] |
| Member of | Terms | [7] |
| Semantic Field | Emotion | [7] |
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.
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hasSynonymbeam/028a6fc6-cd01-4cd2-b721-375cd468d51f
ex:unhappy
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hasSynonymbeam/028a6fc6-cd01-4cd2-b721-375cd468d51f
ex:depressed
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typebeam/028a6fc6-cd01-4cd2-b721-375cd468d51f
ex:string
—
typebeam/29aeb2c2-4d07-4e88-8e96-e87a1c5906a9
ex:Word
—
labelbeam/29aeb2c2-4d07-4e88-8e96-e87a1c5906a9
sad
—
hasSynonymbeam/29aeb2c2-4d07-4e88-8e96-e87a1c5906a9
ex:unhappy
—
hasSynonymbeam/29aeb2c2-4d07-4e88-8e96-e87a1c5906a9
ex:depressed
—
synonymCountbeam/29aeb2c2-4d07-4e88-8e96-e87a1c5906a9
2
—
typebeam/26375e84-be0b-411d-8740-b19721f3bf80
ex:Emotion
—
typebeam/fdf83faa-03c9-4e80-9792-6fa66000e80d
ex:Word
—
typebeam/534be9d2-c97a-4867-8efb-8f090879be4b
ex:String
—
labelbeam/534be9d2-c97a-4867-8efb-8f090879be4b
sad
—
isMemberOfbeam/534be9d2-c97a-4867-8efb-8f090879be4b
ex:words-variable
—
typebeam/add559bf-3ce5-4390-a544-0660ac8acf99
ex:Term
—
semanticCategorybeam/add559bf-3ce5-4390-a544-0660ac8acf99
ex:emotion
—
typebeam/f0cc860e-7f75-4530-abef-84dc82b5e5ad
ex:StringLiteral
—
memberOfbeam/f0cc860e-7f75-4530-abef-84dc82b5e5ad
ex:terms
—
semanticCategorybeam/f0cc860e-7f75-4530-abef-84dc82b5e5ad
ex:negative_emotion
—
semanticFieldbeam/f0cc860e-7f75-4530-abef-84dc82b5e5ad
ex:emotion
—
typebeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
ex:TestTerm
—
typelocomo/f8b47f6b-06dd-4560-9e58-d13ecae1fabc
ex:Emotion
References (9)
9 references
ctx:claims/beam/028a6fc6-cd01-4cd2-b721-375cd468d51f- full textbeam-chunktext/plain1 KB
doc:beam/028a6fc6-cd01-4cd2-b721-375cd468d51fShow excerpt
thesaurus.add_synonym("sad", "unhappy") thesaurus.add_synonym("sad", "depressed") # Test the lookup start_time = time.time() synonyms = thesaurus.get_synonyms("happy") end_time = time.time() print(f"Lookup took {end_time - start_time} seco…
ctx:claims/beam/29aeb2c2-4d07-4e88-8e96-e87a1c5906a9- full textbeam-chunktext/plain1 KB
doc:beam/29aeb2c2-4d07-4e88-8e96-e87a1c5906a9Show excerpt
By following these steps, you can optimize your `/api/v1/synonym-expand` endpoint for better performance using caching and rate limiting. If you have any specific issues or need further customization, feel free to ask! [Turn 10144] User: I…
ctx:claims/beam/26375e84-be0b-411d-8740-b19721f3bf80- full textbeam-chunktext/plain1 KB
doc:beam/26375e84-be0b-411d-8740-b19721f3bf80Show excerpt
4. **Visualizations**: Use visualizations to help identify patterns and outliers in the data. ### Detailed Logging Enhance your logging to capture more details about each lookup: ```python import logging import time logging.basicConfig(…
ctx:claims/beam/fdf83faa-03c9-4e80-9792-6fa66000e80d- full textbeam-chunktext/plain1 KB
doc:beam/fdf83faa-03c9-4e80-9792-6fa66000e80dShow excerpt
logging.basicConfig(level=logging.INFO) def thesaurus_lookup(word): start_time = time.time() # Simulate the lookup time.sleep(0.1) end_time = time.time() logging.info(f"Lookup took {end_time - start_time} seconds") …
ctx:claims/beam/534be9d2-c97a-4867-8efb-8f090879be4b- full textbeam-chunktext/plain1 KB
doc:beam/534be9d2-c97a-4867-8efb-8f090879be4bShow excerpt
logging.info(f"Thesaurus lookup for '{word}' took {end_time - start_time:.6f} seconds") return ["synonym1", "synonym2"] # Test the lookup words = ["happy", "sad", "angry"] * 100 # Simulate a larger dataset for word in words: …
ctx:claims/beam/add559bf-3ce5-4390-a544-0660ac8acf99- full textbeam-chunktext/plain1 KB
doc:beam/add559bf-3ce5-4390-a544-0660ac8acf99Show excerpt
closest_synonyms.extend([synonyms[i] for i in np.argsort(similarities)[-2:]]) # Take top 2 closest synonyms return closest_synonyms # Test the synonym expansion terms = ["happy", "sad", "angry"] for term in terms: synonym…
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/5e1fccc0-109f-4d58-b6c4-6482a168aad7- full textbeam-chunktext/plain1 KB
doc:beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7Show excerpt
for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_contextual_embeddings(syn)) for syn in synonyms] closest_synonyms.extend([synon…
ctx:claims/locomo/f8b47f6b-06dd-4560-9e58-d13ecae1fabc- full textbeam-chunktext/plain3 KB
doc:beam/f8b47f6b-06dd-4560-9e58-d13ecae1fabcShow excerpt
[Session date: 1:17 pm on 28 January, 2023] John: Hey Maria, since we last spoke I went to that community mtg. It was really interesting hearing everyone's worries and how it affects our area. It made me realize how crucial the upgrades are…
See also
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