Triple
T12884485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs Poyser |
E308189
|
entity |
| Predicate | hasMoralViewpoint |
P29043
|
FINISHED |
| Object | emphasis on duty and responsibility |
—
|
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: emphasis on duty and responsibility | Statement: [Mrs Poyser, hasMoralViewpoint, emphasis on duty and responsibility]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMoralViewpoint Context triple: [Mrs Poyser, hasMoralViewpoint, emphasis on duty and responsibility]
-
A.
hasMoralPerspective
chosen
Indicates that an entity holds or applies a particular moral or ethical viewpoint in evaluating actions, situations, or other entities.
-
B.
hasMoralFraming
Indicates that something is presented or interpreted in terms of moral values, judgments, or ethical considerations.
-
C.
hasMoralCharacteristic
Indicates that an entity possesses a particular moral quality, trait, or ethical attribute.
-
D.
hasMoralComplexity
Indicates that the relationship or action involves nuanced ethical considerations, conflicting values, or ambiguity in determining what is morally right or wrong.
-
E.
moralBelief
Indicates that an agent holds a normative judgment about what is right, wrong, good, or bad in a given context.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97c7f91d08190aac2f6419d3ba992 |
completed | April 10, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69d96fa55b888190ab1612e93c41aec4 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:39 p.m.