Triple
T22260868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tarakasura |
E550218
|
entity |
| Predicate | alignmentInMyth |
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: [Tarakasura, alignmentInMyth, evil]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alignmentInMyth Context triple: [Tarakasura, alignmentInMyth, evil]
-
A.
alignmentConcept
Indicates a conceptual or abstract relationship of alignment or correspondence between entities, such as agreement, compatibility, or shared orientation in some dimension.
-
B.
alignmentInSeries
Indicates that one entity’s position or orientation is arranged in a specific way relative to others within an ordered sequence or series.
-
C.
alignmentGoal
Indicates that an entity has a desired or target alignment state it aims to achieve or maintain.
-
D.
alignedAgainst
Indicates that two or more entities are united in opposition to a common target, side, or objective.
-
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_69e11e42adb8819087714772ea606709 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f141b703b081909a2b432463a8e2df |
completed | April 28, 2026, 11:24 p.m. |
| PD | Predicate disambiguation | batch_69e72fe1e0cc8190bd13cff2a0846225 |
completed | April 21, 2026, 8:05 a.m. |
Created at: April 16, 2026, 8:39 p.m.