Triple
T36546804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mortal Men |
E901159
|
entity |
| Predicate | moralRange |
P140806
|
FINISHED |
| Object | capable of both great good and great 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: capable of both great good and great evil | Statement: [Mortal Men, moralRange, capable of both great good and great evil]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: moralRange Context triple: [Mortal Men, moralRange, capable of both great good and great evil]
-
A.
moralCriterion
Indicates that something is being evaluated or classified according to a standard of moral judgment or ethical rightness.
-
B.
moralAttitude
Indicates a subject’s evaluative stance or judgment about the moral rightness or wrongness of another entity, action, or situation.
-
C.
moralConcept
Indicates that one entity represents or embodies a moral or ethical concept in relation to another.
-
D.
moralTendency
chosen
Indicates a general inclination or propensity of an entity to act in ways judged as morally right or wrong.
-
E.
moralTrajectory
Indicates the direction and pattern of change in an entity’s moral behavior or ethical stance over time.
- 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_69f76e61217081908b79d610fe67b013 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1baf25c8190a78dd54a400d2c50 |
completed | May 3, 2026, 9:44 p.m. |
Created at: May 3, 2026, 4:11 p.m.