Triple

T864621
Position Surface form Disambiguated ID Type / Status
Subject Reuters E18673 entity
Predicate hasPart P35 FINISHED
Object Reuters Pictures E18673 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: Reuters Pictures | Statement: [Reuters, hasPart, Reuters Pictures]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Reuters Pictures
Context triple: [Reuters, hasPart, Reuters Pictures]
  • A. Getty Images
    Getty Images is a leading global provider of stock photography, editorial imagery, video, and other visual content for media, advertising, and corporate clients.
  • B. Reuters chosen
    Reuters is a major international news agency and media organization known for providing real-time news and financial information to outlets and markets worldwide.
  • C. Photos
    Photos is Apple's built-in photo management and editing application for macOS that organizes, syncs, and enhances users' image and video libraries.
  • D. Metro Pictures
    Metro Pictures was an early 20th-century American film production and distribution company that later merged into Metro-Goldwyn-Mayer (MGM), helping form one of Hollywood’s major studios.
  • E. Adobe Stock
    Adobe Stock is Adobe’s royalty-free stock content service offering millions of photos, illustrations, vectors, videos, and other creative assets for use in design and media projects.
  • 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_69a4938ce8688190a24bdfef82ba7d21 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ac6acc148190bcc00a1e939ace77 completed March 1, 2026, 9:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7a3c7999c81908b2f27610c263b7f completed March 4, 2026, 3:15 a.m.
Created at: March 1, 2026, 7:39 p.m.