Triple

T21778977
Position Surface form Disambiguated ID Type / Status
Subject The Tale of Pigling Bland E537656 entity
Predicate featuresCharacter P626 FINISHED
Object Pigling Bland 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: Pigling Bland | Statement: [The Tale of Pigling Bland, featuresCharacter, Pigling Bland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pigling Bland
Context triple: [The Tale of Pigling Bland, featuresCharacter, Pigling Bland]
  • A. Pigling Bland chosen
    Pigling Bland is a fictional pig character created by Beatrix Potter, featured as the protagonist of her children's story "The Tale of Pigling Bland."
  • B. The Pig
    The Pig is a colloquial nickname for the U.S. military’s M60 general-purpose machine gun, known for its heavy weight, high rate of fire, and extensive use during the Vietnam War.
  • C. Podgy Pig
    Podgy Pig is a plump, good-natured pig character who appears as one of Rupert Bear’s close friends in the classic British children’s stories.
  • D. Pig-wig
    Pig-wig is a spirited young pig character from Beatrix Potter’s children’s stories, known for her adventurous escape alongside Pigling Bland.
  • E. Pig
    "Pig" is a 2018 Iranian black comedy film directed by Mani Haghighi that satirizes the country’s film industry and censorship.
  • 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_69e0c470759c819094a215757113562b completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69f0462bde288190beef8c4388949132 completed April 28, 2026, 5:31 a.m.
Created at: April 16, 2026, 6:52 p.m.