Triple

T5401232
Position Surface form Disambiguated ID Type / Status
Subject Green Goblin E120781 entity
Predicate enemyOf P437 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: [Green Goblin, enemyOf, Gwen Stacy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gwen Stacy
Context triple: [Green Goblin, enemyOf, 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_69bd46391c0c81909fa484446732b6a3 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd8771b25c819080da247bc3164cd9 completed March 20, 2026, 5:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf3383b360819087447e6813c45edf completed March 22, 2026, 12:10 a.m.
Created at: March 20, 2026, 2:04 p.m.