Triple
T2309593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dioscorus |
E51920
|
entity |
| Predicate | moralRoleInTexts |
P25343
|
FINISHED |
| Object | negative example of impiety and cruelty |
—
|
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: negative example of impiety and cruelty | Statement: [Dioscorus, moralRoleInTexts, negative example of impiety and cruelty]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: moralRoleInTexts Context triple: [Dioscorus, moralRoleInTexts, negative example of impiety and cruelty]
-
A.
moralTheme
chosen
Indicates that a work, event, or situation embodies or conveys a particular ethical lesson, value, or moral principle.
-
B.
derivesMoralityFrom
Indicates that one entity bases or grounds its moral principles, judgments, or ethical framework on another entity.
-
C.
moralImplication
Indicates that one situation, action, or state of affairs entails or suggests a particular moral judgment, obligation, or ethical consequence.
-
D.
hasMoralPerspective
Indicates that an entity holds or applies a particular moral or ethical viewpoint in evaluating actions, situations, or other entities.
-
E.
moralStatus
Indicates the ethical standing or degree of moral consideration that one entity has in relation to another.
- 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_69a88b0bb30c81908ded03b006d29387 |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abce1f4f0c8190a714e4dcb8449f7e |
completed | March 7, 2026, 7:05 a.m. |
| PD | Predicate disambiguation | batch_69abc58ce2a081908ce2f0cadd92e9f8 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:49 p.m.