Triple

T5305969
Position Surface form Disambiguated ID Type / Status
Subject Bausch + Lomb E120103 entity
Predicate formerName P65 FINISHED
Object Bausch & Lomb E120103 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: Bausch & Lomb | Statement: [Bausch + Lomb, formerName, Bausch & Lomb]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bausch & Lomb
Context triple: [Bausch + Lomb, formerName, Bausch & Lomb]
  • A. Bausch + Lomb chosen
    Bausch + Lomb is a global eye health company best known for its contact lenses, lens care products, and ophthalmic pharmaceuticals.
  • B. Allergan
    Allergan is a global pharmaceutical company best known for developing branded drugs and medical aesthetics products, including Botox.
  • C. American Optical Company
    American Optical Company is a historic U.S. manufacturer best known for producing eyeglasses, optical instruments, and pioneering lens technologies.
  • D. Optica
    Optica is a leading scientific society dedicated to advancing the study and application of optics and photonics worldwide.
  • E. Eyetech Group Ltd
    Eyetech Group Ltd was a UK-based computer hardware company best known for manufacturing AmigaOne systems for the Amiga enthusiast market.
  • 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_69bd44704be88190acdb2ac481b0ff55 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd851dd0d081909c0bf1423278ad8a completed March 20, 2026, 5:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf10f77b048190b8d3b39b900008d8 completed March 21, 2026, 9:43 p.m.
Created at: March 20, 2026, 1:53 p.m.