Triple

T15833694
Position Surface form Disambiguated ID Type / Status
Subject AQUOS E383931 entity
Predicate hasDisplayTechnology P6774 FINISHED
Object LCD E845749 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: LCD | Statement: [AQUOS, hasDisplayTechnology, LCD]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: LCD
Context triple: [AQUOS, hasDisplayTechnology, LCD]
  • A. LCD chosen
    An LCD (liquid crystal display) is a flat-panel screen technology commonly used to visually present information on electronic devices.
  • B. LCD TV
    An LCD TV is a flat-panel television that uses liquid crystal display technology with a backlight to produce images, widely known for its affordability and broad market presence compared to newer display types.
  • C. DLP technology
    DLP technology is a digital display and projection system that uses microscopic mirrors to modulate light and create high-quality images in projectors and related devices.
  • D. VDU
    VDU is the Lithuanian abbreviation for Vytautas Magnus University, a prominent public university in Kaunas, Lithuania.
  • E. LED
    LED (light-emitting diode) is a highly efficient, long-lasting solid-state light source widely used in applications such as automotive headlamps, displays, and general illumination.
  • 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_69d86da34c888190976e06c4019d415a completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e11e6670d48190a456581dd951f168 completed April 16, 2026, 5:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffa137be2c81909c8f04b5cc1a5b21 completed May 9, 2026, 9:03 p.m.
Created at: April 10, 2026, 4:49 a.m.