Triple

T374239
Position Surface form Disambiguated ID Type / Status
Subject Marc Andreessen E8334 entity
Predicate employer P7 FINISHED
Object Opsware E47370 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: Opsware | Statement: [Marc Andreessen, employer, Opsware]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Opsware
Context triple: [Marc Andreessen, employer, Opsware]
  • A. Opsware chosen
    Opsware was a data center automation and IT infrastructure management software company, best known for being co-founded by Marc Andreessen and later acquired by Hewlett-Packard.
  • B. BEA Systems
    BEA Systems was a software company best known for its enterprise middleware and application server products that played a major role in early Java-based web and enterprise computing.
  • C. Lucent Technologies
    Lucent Technologies was a major American telecommunications equipment company, spun off from AT&T, known for its Bell Labs research arm and contributions to networking and communications technology.
  • D. Siebel Systems
    Siebel Systems was a leading enterprise software company best known for pioneering customer relationship management (CRM) solutions for large organizations.
  • E. Cisco Systems
    Cisco Systems is a multinational technology conglomerate best known for designing and selling networking hardware, software, and telecommunications equipment used worldwide.
  • 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_69a2e7f2ec648190b42bc7db424f8109 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ec13b9b48190b294d998c6720132 completed Feb. 28, 2026, 1:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3f4da5b2881909f71546e714c7883 completed March 1, 2026, 8:12 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.