Triple

T11657489
Position Surface form Disambiguated ID Type / Status
Subject Sharp E277046 entity
Predicate brand P1500 FINISHED
Object AQUOS E383931 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: AQUOS | Statement: [Sharp, brand, AQUOS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AQUOS
Context triple: [Sharp, brand, AQUOS]
  • A. AQUOS brand televisions chosen
    AQUOS brand televisions are Sharp Corporation’s line of high-definition and smart TVs known for their advanced display technology and sleek designs.
  • B. Bravia
    Bravia is Sony's line of high-definition televisions and display products known for their advanced picture and sound technologies.
  • C. Toshiba REGZA
    Toshiba REGZA is a line of high-definition televisions and display devices produced by Toshiba, known for their advanced picture processing and multimedia features.
  • D. Xperia
    Xperia is Sony's line of smartphones and tablets known for their sleek design, high-quality displays, and advanced camera technology.
  • E. Sanyo
    Sanyo is a Japanese electronics brand known for producing a wide range of consumer and industrial electronic products, including televisions, batteries, and home appliances.
  • 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_69d6aafbb3c081908a9cdb4ecb8d981d completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a3d0331481909682b2e504e4c9a0 completed April 10, 2026, 7:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef138faf4c81908043c71550048d75 completed April 27, 2026, 7:43 a.m.
Created at: April 8, 2026, 9:39 p.m.