Triple

T1831441
Position Surface form Disambiguated ID Type / Status
Subject Fedora Sericea E40769 entity
Predicate sponsor P67 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: [Fedora Sericea, sponsor, Red Hat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Red Hat
Context triple: [Fedora Sericea, sponsor, 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_69a8864644bc8190b2358ab897194ac1 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb022aef48190975b6d12fc6681ad completed March 7, 2026, 4:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69adc9b24f448190a3aa5a85a9106d71 completed March 8, 2026, 7:10 p.m.
Created at: March 4, 2026, 7:32 p.m.