Triple

T13468546
Position Surface form Disambiguated ID Type / Status
Subject John W. Thompson E311568 entity
Predicate employer P7 FINISHED
Object Symantec E192676 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: Symantec | Statement: [John W. Thompson, employer, Symantec]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Symantec
Context triple: [John W. Thompson, employer, Symantec]
  • A. Symantec chosen
    Symantec is a cybersecurity and software company best known for its Norton antivirus products and enterprise security solutions.
  • B. McAfee
    McAfee is a global cybersecurity company best known for its antivirus and digital security software for consumers and businesses.
  • C. Trend Micro
    Trend Micro is a global cybersecurity company known for its antivirus, cloud security, and enterprise threat protection solutions.
  • D. Sophos
    Sophos is a British cybersecurity company known for providing antivirus, endpoint protection, and network security solutions to businesses and organizations worldwide.
  • E. Veritas Software
    Veritas Software was a prominent enterprise data management and storage software company known for its backup, recovery, and availability solutions before being acquired by Symantec.
  • 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_69d806a938b8819097ec43a2229fc7f9 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbaf21e46081908a00c9acf54f270f completed April 12, 2026, 2:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69f74629f1408190b54194fe794be39a completed May 3, 2026, 12:57 p.m.
Created at: April 9, 2026, 9:42 p.m.