Triple

T15989436
Position Surface form Disambiguated ID Type / Status
Subject Scott Banister E387784 entity
Predicate employer P7 FINISHED
Object IronPort E1187500 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: IronPort | Statement: [Scott Banister, employer, IronPort]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: IronPort
Context triple: [Scott Banister, employer, IronPort]
  • A. IronPort chosen
    IronPort is an email and web security company best known for its high-performance anti-spam and anti-malware gateway appliances, later acquired by Cisco Systems.
  • B. Barracuda Networks
    Barracuda Networks is a cybersecurity and data protection company known for providing email security, network security, and backup solutions for businesses.
  • C. Sophos
    Sophos is a British cybersecurity company known for providing antivirus, endpoint protection, and network security solutions to businesses and organizations worldwide.
  • D. Blue Coat Systems
    Blue Coat Systems was a cybersecurity company best known for its web security and WAN optimization solutions, later integrated into Symantec’s enterprise security portfolio after its acquisition.
  • E. Palo Alto Networks
    Palo Alto Networks is a leading global cybersecurity company known for its next-generation firewalls, cloud security solutions, and threat intelligence services.
  • 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_69d86daa562c81908aacc179c0fe8fb5 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e157829ec08190aa4a683e29a0148a completed April 16, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffdbc8a8a081908ac1b431f524d650 completed May 10, 2026, 1:13 a.m.
Created at: April 10, 2026, 4:54 a.m.