Triple

T20855633
Position Surface form Disambiguated ID Type / Status
Subject Avaya Stadium E513471 entity
Predicate sponsor P67 FINISHED
Object Avaya NE NERFINISHED

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: Avaya | Statement: [Avaya Stadium, sponsor, Avaya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Avaya
Context triple: [Avaya Stadium, sponsor, Avaya]
  • A. Avaya chosen
    Avaya is an American multinational technology company specializing in business communications, unified communications, and contact center solutions for enterprises and organizations worldwide.
  • B. Polycom
    Polycom is a telecommunications company best known for its audio and video conferencing solutions and collaboration technologies used in businesses worldwide.
  • C. Tandberg
    Tandberg is a Norwegian company best known for its video conferencing and telepresence solutions, which became part of Cisco Systems after its acquisition.
  • D. Megaco
    Megaco is a signaling protocol used in telecommunications networks to control media gateways that connect different types of communication systems, such as VoIP and traditional telephone networks.
  • E. Opsware
    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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4f5b01081909452f654d2fc3f50 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c3a81ac4819084a07625b8ed4ec5 completed April 21, 2026, 12:24 a.m.
Created at: April 16, 2026, 12:44 p.m.