Triple
T37592108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Archangel of Wisdom |
E935284
|
entity |
| Predicate | moralityShift |
P66484
|
FINISHED |
| Object | from divine to darker association |
—
|
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: from divine to darker association | Statement: [Archangel of Wisdom, moralityShift, from divine to darker association]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: moralityShift Context triple: [Archangel of Wisdom, moralityShift, from divine to darker association]
-
A.
moralTendency
Indicates a general inclination or propensity of an entity to act in ways judged as morally right or wrong.
-
B.
moralTrajectory
chosen
Indicates the direction and pattern of change in an entity’s moral behavior or ethical stance over time.
-
C.
moralTurningPoint
Indicates a pivotal moment in which an entity undergoes a significant change in moral stance, values, or ethical behavior.
-
D.
moralBelief
Indicates that an agent holds a normative judgment about what is right, wrong, good, or bad in a given context.
-
E.
moralTheme
Indicates that a work, event, or situation embodies or conveys a particular ethical lesson, value, or moral principle.
- 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_69f76ecf39c081909baffe597bb55273 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbb084760c8190a1554985d3c3cb7a |
completed | May 6, 2026, 9:20 p.m. |
| PD | Predicate disambiguation | batch_69fbadf3cb548190ba3b7514f76b790a |
completed | May 6, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:18 p.m.