Triple
T8811917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luther Memorandum |
E209683
|
entity |
| Predicate | ethicalCharacterization |
P47751
|
FINISHED |
| Object | crime against humanity |
—
|
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: crime against humanity | Statement: [Luther Memorandum, ethicalCharacterization, crime against humanity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ethicalCharacterization Context triple: [Luther Memorandum, ethicalCharacterization, crime against humanity]
-
A.
characterAlignment
Indicates the moral or ethical stance a character holds, typically along axes such as good–evil and lawful–chaotic.
-
B.
moralCriterion
Indicates that something is being evaluated or classified according to a standard of moral judgment or ethical rightness.
-
C.
moralAttitude
Indicates a subject’s evaluative stance or judgment about the moral rightness or wrongness of another entity, action, or situation.
-
D.
hasMoralCharacteristic
chosen
Indicates that an entity possesses a particular moral quality, trait, or ethical attribute.
-
E.
moralConcept
Indicates that one entity represents or embodies a moral or ethical concept 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_69ca8363f3308190a47e3f1ebd51f613 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5feed07881909bbe116ae359346a |
completed | March 31, 2026, 11:59 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1f28ec8190a34311cb412920c2 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:45 p.m.