Triple

T20681456
Position Surface form Disambiguated ID Type / Status
Subject The Many Adventures of Winnie the Pooh E508301 entity
Predicate voiceActor P1507 FINISHED
Object Ralph Wright 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: Ralph Wright | Statement: [The Many Adventures of Winnie the Pooh, voiceActor, Ralph Wright]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ralph Wright
Context triple: [The Many Adventures of Winnie the Pooh, voiceActor, Ralph Wright]
  • A. Ralph Wright chosen
    Ralph Wright was an American animator and writer best known for his story and screenwriting work on numerous classic Disney films.
  • B. Ralph Walker
    Ralph Walker was a British civil engineer and architect known for his influential work on major dock projects in London during the late 18th and early 19th centuries.
  • C. Ralph Walker
    Ralph Walker was a prominent early 20th-century American architect known for his influential Art Deco skyscraper designs in New York City.
  • D. Ralph Partridge
    Ralph Partridge was an English editor and member of the Bloomsbury Group, closely associated with figures like Lytton Strachey and Dora Carrington.
  • E. Ralph Hart
    Ralph Hart is an actor best known for his role on the classic American television sitcom "The Lucy Show."
  • 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_69e0b4c1164881909a3bf1e3ddb2bc32 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6bea842dc81908106a0c29d1577aa completed April 21, 2026, 12:02 a.m.
Created at: April 16, 2026, 11:45 a.m.