Triple

T19454905
Position Surface form Disambiguated ID Type / Status
Subject Hawkins National Laboratory E486709 entity
Predicate moralAlignmentInStory P22459 FINISHED
Object evil 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: evil | Statement: [Hawkins National Laboratory, moralAlignmentInStory, evil]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: moralAlignmentInStory
Context triple: [Hawkins National Laboratory, moralAlignmentInStory, evil]
  • A. characterAlignment chosen
    Indicates the moral or ethical stance a character holds, typically along axes such as good–evil and lawful–chaotic.
  • B. ethicalStanceInStory
    Indicates the ethical position, judgment, or moral viewpoint expressed or taken within the context of a particular story or narrative.
  • C. moralNarrativeRole
    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. isMoralFoilFor
    Indicates that one entity serves as a contrasting counterpart whose differing moral qualities highlight or emphasize the moral traits of another entity.
  • E. hasMoralArchetype
    Indicates that an entity exemplifies or is characterized by a particular moral pattern, role, or ethical archetype.
  • 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_69d8e8d86d608190bd199a98d0297f27 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e633c117ac8190a38c01c3191beaea completed April 20, 2026, 2:10 p.m.
PD Predicate disambiguation batch_69e4fd7499a4819082bec0be8afba35c completed April 19, 2026, 4:06 p.m.
Created at: April 10, 2026, 1:38 p.m.