Triple
T9845855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Good Witch of the South |
E239339
|
entity |
| Predicate | hasMoralRole |
P9237
|
FINISHED |
| Object | mentor figure |
—
|
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: mentor figure | Statement: [Good Witch of the South, hasMoralRole, mentor figure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMoralRole Context triple: [Good Witch of the South, hasMoralRole, mentor figure]
-
A.
hasMoralCharacteristic
Indicates that an entity possesses a particular moral quality, trait, or ethical attribute.
-
B.
hasEthicalRole
chosen
Indicates that an entity holds a position, function, or responsibility defined in terms of ethical duties, norms, or moral obligations in relation to another entity or context.
-
C.
hasMoralPerspective
Indicates that an entity holds or applies a particular moral or ethical viewpoint in evaluating actions, situations, or other entities.
-
D.
hasMoralFunction
Indicates that an entity serves or fulfills a role related to moral or ethical considerations.
-
E.
hasMoralComplexity
Indicates that the relationship or action involves nuanced ethical considerations, conflicting values, or ambiguity in determining what is morally right or wrong.
- 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_69ca84e3f0c48190ada72a65ebd50efd |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb35ff7848190a8a717773d8654b9 |
completed | April 2, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69cd03e57cac8190914bb5ae608a6e0e |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:34 p.m.