Triple

T15064343
Position Surface form Disambiguated ID Type / Status
Subject Herbert Walther Award E379716 entity
Predicate presentedBy P83 FINISHED
Object Optica E4713 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: Optica | Statement: [Herbert Walther Award, presentedBy, Optica]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Optica
Context triple: [Herbert Walther Award, presentedBy, Optica]
  • A. Optica chosen
    Optica is a leading scientific society dedicated to advancing the study and application of optics and photonics worldwide.
  • B. American Optical Company
    American Optical Company is a historic U.S. manufacturer best known for producing eyeglasses, optical instruments, and pioneering lens technologies.
  • C. Opti
    Opti is a friendly, futuristic robot character that served as one of the official mascots of Expo 2020 Dubai.
  • D. ZEISS
    ZEISS is a renowned German optics company best known for its high-quality lenses and imaging technologies used in cameras, microscopes, and industrial systems.
  • E. Dioptrique
    Dioptrique is a scientific treatise by René Descartes that lays out his pioneering theories on light and optics, including the law of refraction.
  • 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_69d85cd7683881908d405c1b5d7b4f7f completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69dedee803ac81908bb7d66e49c2eb72 completed April 15, 2026, 12:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69fea5c8b3ac8190b8fc921b6e6eeed5 completed May 9, 2026, 3:11 a.m.
Created at: April 10, 2026, 3:02 a.m.