Triple

T2629760
Position Surface form Disambiguated ID Type / Status
Subject Cygwin E59603 entity
Predicate developer P73 FINISHED
Object Red Hat E5668 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: Red Hat | Statement: [Cygwin, developer, Red Hat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Red Hat
Context triple: [Cygwin, developer, Red Hat]
  • A. Red Hat chosen
    Red Hat is a leading American open-source software company best known for its enterprise Linux distribution and related cloud and middleware solutions.
  • B. Red Hat Enterprise Linux
    Red Hat Enterprise Linux is a commercially supported, enterprise-grade Linux distribution widely used for servers, cloud deployments, and mission-critical applications.
  • C. SUSE
    SUSE is a German-based open-source software company best known for its enterprise Linux distributions and related infrastructure solutions.
  • D. Novell
    Novell was a prominent software company best known for its NetWare network operating system and contributions to enterprise networking and Linux technologies.
  • E. Fedora
    Fedora is a 1978 drama film by Billy Wilder that explores the tragic mystique and hidden costs of Hollywood stardom.
  • 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_69ab4ac8596c8190b34997e73d9e991c completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abd8c452508190b02e1630d725497a completed March 7, 2026, 7:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69af90a7021081909f81c4ddb48fa00c completed March 10, 2026, 3:31 a.m.
Created at: March 6, 2026, 9:50 p.m.