Triple

T1224347
Position Surface form Disambiguated ID Type / Status
Subject Ward Cunningham E26292 entity
Predicate employer P7 FINISHED
Object Tektronix E7900 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: Tektronix | Statement: [Ward Cunningham, employer, Tektronix]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tektronix
Context triple: [Ward Cunningham, employer, Tektronix]
  • A. Tektronix chosen
    Tektronix is an American company best known for designing and manufacturing electronic test and measurement equipment such as oscilloscopes and signal analyzers.
  • B. Agilent Technologies
    Agilent Technologies is a global company specializing in life sciences, diagnostics, and analytical laboratory instruments and services.
  • C. Beckman Instruments
    Beckman Instruments was an American scientific instruments company known for pioneering analytical and laboratory equipment used in chemistry, biology, and electronics research.
  • D. Ampex
    Ampex is an American electronics company renowned for pioneering professional audio and video tape recording technology.
  • E. Cambridge Scientific Instrument Company
    Cambridge Scientific Instrument Company was a pioneering British firm based in Cambridge that became renowned in the late 19th and early 20th centuries for designing and manufacturing precision scientific instruments for research and education.
  • 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_69a49484688c8190a1bf285eb396a8b6 completed March 1, 2026, 7:33 p.m.
NER Named-entity recognition batch_69a4be382c4081909238c18b805352a7 completed March 1, 2026, 10:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac832704c08190a1a73ebd90fa91b8 completed March 7, 2026, 7:57 p.m.
Created at: March 1, 2026, 7:47 p.m.