Triple

T664296
Position Surface form Disambiguated ID Type / Status
Subject Orange Bowl E12824 entity
Predicate previousSponsor P17776 FINISHED
Object Vizio E77119 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: Vizio | Statement: [Orange Bowl, previousSponsor, Vizio]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vizio
Context triple: [Orange Bowl, previousSponsor, Vizio]
  • A. Vizio chosen
    Vizio is an American consumer electronics company best known for its affordable flat-screen televisions and home entertainment products.
  • B. Bravia
    Bravia is Sony's line of high-definition televisions and display products known for their advanced picture and sound technologies.
  • C. Roku
    Roku is a popular digital media player platform that streams internet-based television, movies, and other content through apps and channels on connected TVs.
  • D. LG Electronics
    LG Electronics is a South Korean multinational electronics company known for producing a wide range of consumer electronics, home appliances, and mobile devices.
  • E. Toshiba
    Toshiba is a major Japanese multinational conglomerate known for its electronics, semiconductors, and information technology products and 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_69a493355dec819098d4244b2fa34885 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a4a517ac148190aa032b77885bf709 completed March 1, 2026, 8:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5c39abcbc819093797700dc32d25a completed March 2, 2026, 5:06 p.m.
Created at: March 1, 2026, 7:36 p.m.