Triple
T7324737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sciron |
E168841
|
entity |
| Predicate | alignmentInMyths |
P74485
|
FINISHED |
| Object | evil |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: evil | Statement: [Sciron, alignmentInMyths, evil]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alignmentInMyths Context triple: [Sciron, alignmentInMyths, evil]
-
A.
linkedToMythology
Indicates that something has a connection or association with a mythological tradition, figure, story, or theme.
-
B.
mythologicalCategory
Indicates that one entity is classified as belonging to the mythological type, group, or category represented by the other entity.
-
C.
mythologicalRole
Indicates the specific function, duty, or status an entity holds within a mythological or legendary context.
-
D.
settingOfMyth
Indicates that a location or environment serves as the backdrop or context in which a particular myth takes place.
-
E.
alignmentInStory
chosen
Indicates how a character’s moral or ethical stance (e.g., good, neutral, evil) is portrayed within the context of a specific story.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c68a54cacc81908e3b773441f19566 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f04993408190b73fb46d83a632d5 |
completed | March 27, 2026, 9:02 p.m. |
| PD | Predicate disambiguation | batch_69c6e77230048190b2c29ca6b3a65b8e |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 3:03 p.m.