Triple
T17951166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Svarga |
E448833
|
entity |
| Predicate | opposedMoralFunction |
P129862
|
FINISHED |
| Object | contrast to punishment in Naraka |
—
|
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: contrast to punishment in Naraka | Statement: [Svarga, opposedMoralFunction, contrast to punishment in Naraka]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: opposedMoralFunction Context triple: [Svarga, opposedMoralFunction, contrast to punishment in Naraka]
-
A.
hasMoralFunction
Indicates that an entity serves or fulfills a role related to moral or ethical considerations.
-
B.
moralAttitude
Indicates a subject’s evaluative stance or judgment about the moral rightness or wrongness of another entity, action, or situation.
-
C.
moralDistinction
Indicates that a moral difference or contrast is being drawn between two entities, actions, or states.
-
D.
isMoralFoilFor
Indicates that one entity serves as a contrasting counterpart whose differing moral qualities highlight or emphasize the moral traits of another entity.
-
E.
hasMoralConflictAbout
Indicates that an entity experiences internal ethical tension, doubt, or disagreement regarding another entity, action, or situation.
- F. None of above. chosen
Provenance (4 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_69d8b9f8cca8819099836916c56b7c95 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4afac95048190a5d1ef012899c62b |
completed | April 19, 2026, 10:34 a.m. |
| PD | Predicate disambiguation | batch_69e3f8f2bd088190b1e22ad4d9cc8b13 |
completed | April 18, 2026, 9:34 p.m. |
| PDg | Predicate description generation | batch_69e42d8d68288190a05dc5d7803cf823 |
completed | April 19, 2026, 1:19 a.m. |
Created at: April 10, 2026, 10:21 a.m.