Triple

T17919324
Position Surface form Disambiguated ID Type / Status
Subject Don Woods E448023 entity
Predicate employer P7 FINISHED
Object Xerox 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: Xerox | Statement: [Don Woods, employer, Xerox]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xerox
Context triple: [Don Woods, employer, Xerox]
  • A. Xerox chosen
    Xerox is an American corporation best known for pioneering photocopiers and influential computing innovations, including early graphical user interfaces and office software.
  • B. Ricoh
    Ricoh is a Japanese multinational imaging and electronics company best known for its cameras, printers, copiers, and office equipment solutions.
  • C. Konica Minolta
    Konica Minolta is a Japanese multinational technology company best known for its imaging products, including printers, copiers, and optical devices.
  • D. Kodak Alaris
    Kodak Alaris is a company that focuses on imaging and photographic products and services, including film, photo printing, and document management solutions, formed from the former consumer imaging assets of Eastman Kodak.
  • E. Xerox Network Systems
    Xerox Network Systems (XNS) is a pioneering suite of network protocols developed by Xerox in the late 1970s that strongly influenced later networking technologies and protocol stacks.
  • 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_69d8b9f6d394819082a6d69fd1e23d2f completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e4a30844548190b7a43c2f093f35d7 completed April 19, 2026, 9:40 a.m.
Created at: April 10, 2026, 10:20 a.m.