Triple

T5494442
Position Surface form Disambiguated ID Type / Status
Subject Spider-Man E123778 entity
Predicate loveInterest P7325 FINISHED
Object Gwen Stacy E378748 NE 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: Gwen Stacy | Statement: [Spider-Man, loveInterest, Gwen Stacy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gwen Stacy
Context triple: [Spider-Man, loveInterest, Gwen Stacy]
  • A. Gwen Stacy chosen
    Gwen Stacy is a Marvel Comics character who, in various universes, becomes the superhero Spider-Woman (often called Spider-Gwen), known for her close connection to Spider-Man and her role as a key heroine in the Spider-Verse stories.
  • B. Mary Jane Watson
    Mary Jane Watson is a central character in the Spider-Man franchise, best known as Peter Parker’s longtime love interest and a key emotional anchor in his story.
  • C. Selina Kyle
    Selina Kyle is a cunning and morally ambiguous cat burglar in the Batman universe, best known by her alter ego Catwoman.
  • D. Aunt May
    Aunt May is Peter Parker’s loving and morally grounded aunt who serves as a key emotional anchor and guiding influence in the Spider-Man stories.
  • E. Amanda Reed
    Amanda Reed was the benefactor whose bequest and vision led to the establishment of Reed College in Portland, Oregon.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69bd464a2d908190869324ce176779c8 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd9294ea9c8190b394a8528c385ee9 completed March 20, 2026, 6:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf6c95ba008190ad61e1d31ae38d98 completed March 22, 2026, 4:14 a.m.
Created at: March 20, 2026, 2:10 p.m.