Triple

T22229671
Position Surface form Disambiguated ID Type / Status
Subject Shailendra E549432 entity
Predicate workedOn P3 FINISHED
Object Boot Polish 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: Boot Polish | Statement: [Shailendra, workedOn, Boot Polish]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Boot Polish
Context triple: [Shailendra, workedOn, Boot Polish]
  • A. Boot Polish chosen
    Boot Polish is a 1954 Hindi-language Indian film directed by Prakash Arora and produced by Raj Kapoor, known for its social themes and portrayal of orphaned children struggling against poverty.
  • B. Kiwi (shoe care)
    Kiwi (shoe care) is a globally recognized brand of shoe polish and related footwear care products known for preserving and shining leather shoes.
  • C. Ferodo
    Ferodo is a well-known automotive brand specializing in brake pads and friction products, owned by Federal-Mogul.
  • D. Brillo
    Brillo is a lightweight, Android-based operating system developed by Google for powering and managing Internet of Things (IoT) devices.
  • E. Brush
    Brush is a surname most notably associated with Charles F. Brush, an American inventor and pioneer in electric lighting.
  • 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_69e11e4102b881909cf47d3768e25c19 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f12bf173308190a3d21bfc59b39728 completed April 28, 2026, 9:51 p.m.
Created at: April 16, 2026, 8:37 p.m.