Triple

T1478396
Position Surface form Disambiguated ID Type / Status
Subject Silicon Forest E30894 entity
Predicate majorCompanyPresent P17483 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: [Silicon Forest, majorCompanyPresent, Tektronix]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tektronix
Context triple: [Silicon Forest, majorCompanyPresent, 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. National Instruments
    National Instruments is an American company that develops automated test and measurement systems, best known for its LabVIEW graphical programming environment and modular instrumentation hardware.
  • C. Agilent Technologies
    Agilent Technologies is a global company specializing in life sciences, diagnostics, and analytical laboratory instruments and services.
  • D. Beckman Instruments
    Beckman Instruments was an American scientific instruments company known for pioneering analytical and laboratory equipment used in chemistry, biology, and electronics research.
  • E. Ampex
    Ampex is an American electronics company renowned for pioneering professional audio and video tape recording technology.
  • 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_69a498fe55a88190ab7f9e40ace88e49 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c9e02c188190b87c0aac939eafdd completed March 1, 2026, 11:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad15aff1288190a3e36324d975d482 completed March 8, 2026, 6:22 a.m.
Created at: March 1, 2026, 8:11 p.m.