Triple
T27058124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Dwarf |
E684957
|
entity |
| Predicate | narratorMoralAlignment |
P57036
|
FINISHED |
| Object | malevolent |
—
|
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: malevolent | Statement: [The Dwarf, narratorMoralAlignment, malevolent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: narratorMoralAlignment Context triple: [The Dwarf, narratorMoralAlignment, malevolent]
-
A.
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.
-
B.
narratorType
Indicates the narrative perspective or role from which a story or account is being told.
-
C.
narratorRole
Indicates that one entity serves as the narrator of another entity (such as a story, text, or media work), specifying the narrative role or function it performs.
-
D.
characterAlignment
Indicates the moral or ethical stance a character holds, typically along axes such as good–evil and lawful–chaotic.
-
E.
narratorBasedOn
Indicates that a narrative’s narrator is modeled on, inspired by, or derived from a particular real or fictional 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_69ef14829fac8190914bef9ecc3005d7 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f66c5c13808190887180099745673b |
completed | May 2, 2026, 9:27 p.m. |
| PD | Predicate disambiguation | batch_69f66abddc448190a488852f8abdeb2c |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 27, 2026, 8:19 a.m.