Triple
T26988637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Woodbury militia |
E679800
|
entity |
| Predicate | moralityInNarrative |
P57036
|
FINISHED |
| Object | morally ambiguous |
—
|
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: morally ambiguous | Statement: [Woodbury militia, moralityInNarrative, morally ambiguous]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: moralityInNarrative Context triple: [Woodbury militia, moralityInNarrative, morally ambiguous]
-
A.
moralPortrayal
Indicates how an entity is depicted in terms of moral qualities, such as virtue, vice, or ethical standing, within a given context.
-
B.
moralTheme
Indicates that a work, event, or situation embodies or conveys a particular ethical lesson, value, or moral principle.
-
C.
moralNarrativeRole
chosen
Indicates the role an entity plays within a moral storyline or ethical framing, such as being portrayed as virtuous, villainous, victimized, or morally ambiguous.
-
D.
moralContent
Indicates that something contains, expresses, or conveys moral values, judgments, or ethical significance.
-
E.
derivesMoralityFrom
Indicates that one entity bases or grounds its moral principles, judgments, or ethical framework on another entity.
- 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_69eeeb5138ac8190b3c273ddc659a54f |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651a931748190a637e631a52bbfaa |
completed | May 2, 2026, 7:34 p.m. |
Created at: April 27, 2026, 6:50 a.m.