Triple

T21717473
Position Surface form Disambiguated ID Type / Status
Subject Skippy E536066 entity
Predicate basedOn P98 FINISHED
Object Skippy (comic strip) NE NERFINISHED

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: Skippy (comic strip) | Statement: [Skippy, basedOn, Skippy (comic strip)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Skippy (comic strip)
Context triple: [Skippy, basedOn, Skippy (comic strip)]
  • A. Skippy
    Skippy is a popular American brand best known for its peanut butter products.
  • B. Skippy chosen
    Skippy is a 1931 American comedy-drama film, based on a popular comic strip, that earned Jackie Cooper an Academy Award nomination as one of Hollywood’s earliest child stars.
  • C. Skippy (dog)
    Skippy was a famous Wire Fox Terrier actor best known for appearing as Asta in the 1930s Thin Man film series.
  • D. Shoestring Strip
    Shoestring Strip was the narrow, elongated area of Los Angeles that once connected the city proper to the Port of Los Angeles before being renamed Harbor Gateway.
  • E. Whizzer and Chips
    Whizzer and Chips was a popular British weekly children's comic magazine known for its two-in-one format featuring rival comic sections and characters.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c46c6dd88190a595375fa6ebd701 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69efd96babdc81908226ec043dbe7431 completed April 27, 2026, 9:47 p.m.
Created at: April 16, 2026, 6:47 p.m.