Creation From Chaos vs World Egg Type Lookup
Retain independently annotated creation account motifs, including co-occurrence. Independently annotated feature columns in order: primordial chaos account, world egg account. Compatible classes are not established identities; unknown evidence retained. No automatic semantic parsing, diagnosis, authenticity or safety determination.
Description
Creation narratives can use primordial chaos or a world-egg motif.
Creation narratives can use primordial chaos or a world-egg motif.1
Independently annotated features help compare those narrative forms without judging their truth or claiming a shared lineage. Use this tool to compare independently annotated features with the stated candidate rules.
When to use Creation From Chaos vs World Egg Type Lookup
Use this tool to compare independently annotated features with the stated candidate rules. Identify the community, narrative version, source edition and interpretation context; prepare any annotations independently.
How to use Creation From Chaos vs World Egg Type Lookup and understand its results
- Identify the community, narrative version, source edition and interpretation context; prepare any annotations independently.
- Enter observedFeatureFlags, featureKnownFlags, using the field meanings and rules below, then run the tool.
- Read result.compatibleClasses, result.witnessedClasses, result.unknownFeatureIndices. Interpret the fields using their units, signs and evidence conventions below.
- If input validation fails, correct the reported field and run again. Before applying the result, check reference scope, model assumptions and any missing evidence or placeholder values.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| observedFeatureFlags | Observed feature flags; binary 0/1 under the definitions below | list of number | Required |
| featureKnownFlags | Feature known flags; binary 1=known, 0=unknown, in the order described below | list of number | Required |
Supply binary observation and binary known flags in the fixed column order below. Rule −1 ignores a feature; rule 0 requires known absence and rule 1 requires known presence for a witnessed match. A compatible match permits unknown required features but rejects conflicting known observations. Return every compatible candidate, every fully witnessed candidate and zero-based unknown indices. All-unknown evidence can leave every candidate compatible while witnessing none; incompatible evidence may return no candidates. There is no automatic text interpretation or judgment of truth, authenticity or narrative quality.
| Zero-based column | Observed feature |
|---|---|
| 0 | primordial chaos account |
| 1 | world egg account |
| Candidate | primordial chaos account | world egg account |
|---|---|---|
| chaos-account | Known present (1) | Ignored |
| world-egg-account | Ignored | Known present (1) |
compatible={candidate:all constraints ignored, unknown or matched}; witnessed={candidate:all nonignored constraints known and matched}; unknown={i:known_i=0} Use the exact units, boundary rules, ordering and missing-evidence conventions above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Structured calculation or selected reference, with fields listed below | object |
| result.compatibleClasses | Every fixed candidate whose rules do not conflict with known features | list of string |
| result.witnessedClasses | Every fixed candidate with all nonignored constraints actually known and matched | list of string |
| result.unknownFeatureIndices | Zero-based indices of unknown feature observations | list of values |
{
"input": {
"observedFeatureFlags": [
1,
1
],
"featureKnownFlags": [
1,
1
]
},
"output": {
"result": {
"compatibleClasses": [
"chaos-account",
"world-egg-account"
],
"witnessedClasses": [
"chaos-account",
"world-egg-account"
],
"unknownFeatureIndices": []
}
}
}Limitations and assumptions
Features and criteria are independent annotations. Compatible candidates and retained unknown evidence do not establish an identity, interpretation, overall pass or permission.
All listed fields require explicit values. Unexpected fields, incorrect types, nonfinite numbers and sparse evidence arrays fail validation; the specific bounds and alignment requirements are stated above. Extremely large inputs or outputs can also be rejected. An input error is distinct from a successful result reporting false, no matches or missing evidence.
Alternative or Complementary methods
Use Etiological Origin Myth Type Classifier for this related task: An etiological narrative explains why a feature, place or custom has its present form.
Use Flood Myth Global Distribution Region Lookup when its separate inputs and assumptions fit the question: Flood narratives occur in source-specific religious and cultural settings.
References
Similar or alternative tools
- Etiological Origin Myth Type Classifier
An etiological narrative explains why a feature, place or custom has its present form.
- Flood Myth Global Distribution Region Lookup
Flood narratives occur in source-specific religious and cultural settings.