Triple
T31657669
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Low Shoulder |
E807900
|
entity |
| Predicate | actualMoralStatusInFilm |
P135865
|
FINISHED |
| Object | villainous |
—
|
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: villainous | Statement: [Low Shoulder, actualMoralStatusInFilm, villainous]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: actualMoralStatusInFilm Context triple: [Low Shoulder, actualMoralStatusInFilm, villainous]
-
A.
moralPortrayal
chosen
Indicates how an entity is depicted in terms of moral qualities, such as virtue, vice, or ethical standing, within a given context.
-
B.
ethicalStanceInStory
Indicates the ethical position, judgment, or moral viewpoint expressed or taken within the context of a particular story or narrative.
-
C.
moralStatus
Indicates the ethical standing or degree of moral consideration that one entity has in relation to another.
-
D.
hasMoralFraming
Indicates that something is presented or interpreted in terms of moral values, judgments, or ethical considerations.
-
E.
hasMoralPerspective
Indicates that an entity holds or applies a particular moral or ethical viewpoint in evaluating actions, situations, or other entities.
- 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_69f348daf95c81908b4c985b7ddcd0b3 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a95f453081908414920057d97c90 |
completed | May 3, 2026, 1:48 a.m. |
| PD | Predicate disambiguation | batch_69f6a757c6e081908e37631e5d8d246b |
completed | May 3, 2026, 1:39 a.m. |
Created at: April 30, 2026, 10:56 p.m.