Triple

T21005228
Position Surface form Disambiguated ID Type / Status
Subject The Night Gwen Stacy Died E517396 entity
Predicate impactOnComics P142450 FINISHED
Object increased use of permanent character deaths 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: increased use of permanent character deaths | Statement: [The Night Gwen Stacy Died, impactOnComics, increased use of permanent character deaths]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: impactOnComics
Context triple: [The Night Gwen Stacy Died, impactOnComics, increased use of permanent character deaths]
  • A. comicBookUniverse
    Indicates that multiple comic book characters, stories, or events exist within and are governed by the same fictional continuity or shared narrative world.
  • B. impactOnDirector
    Indicates that one entity has an effect, influence, or consequence on a director in the context of a given situation or action.
  • C. roleInComics
    Indicates that an entity holds a specific role or function within the context of comic books or comic-related works.
  • D. impactOnAuthor
    Indicates that one entity has an effect, influence, or consequence on the author.
  • E. affectedFranchise
    Indicates that one entity has an impact on, or brings about a change in the status or condition of, a franchise.
  • F. None of above. chosen

Provenance (4 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_69e0b50192308190a284fcc89dd23a49 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e6fc3b25ec8190aa4530d1f0bb2b9e completed April 21, 2026, 4:25 a.m.
PD Predicate disambiguation batch_69e5dbec80708190a49bccab7ff97e7b completed April 20, 2026, 7:55 a.m.
PDg Predicate description generation batch_69e5e2df1a888190b5b478e76bdf7fdf completed April 20, 2026, 8:25 a.m.
Created at: April 16, 2026, 1:52 p.m.