Triple

T10003855
Position Surface form Disambiguated ID Type / Status
Subject Kidde E198191 entity
Predicate hasBusinessUnit P7588 FINISHED
Object Kidde Safety E198191 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: Kidde Safety | Statement: [Kidde, hasBusinessUnit, Kidde Safety]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kidde Safety
Context triple: [Kidde, hasBusinessUnit, Kidde Safety]
  • A. Kidde chosen
    Kidde is a well-known manufacturer of fire safety products such as smoke alarms, fire extinguishers, and carbon monoxide detectors.
  • B. Tyco International
    Tyco International was a multinational conglomerate known for its security systems, fire protection, and industrial products before being acquired by Johnson Controls.
  • C. Guardian Industries
    Guardian Industries is a major global manufacturer of glass, automotive, and building products, known for its architectural and float glass operations.
  • D. Whelen Engineering
    Whelen Engineering is an American company that designs and manufactures emergency warning lights, sirens, and related safety systems for police, fire, EMS, and other public safety and automotive applications.
  • E. Honeywell
    Honeywell is a multinational conglomerate best known for its aerospace systems, building technologies, performance materials, and industrial automation products.
  • 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_69ca830fcca48190bbbd9b20c233835f completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cdcd141ec08190b857fe7e15a8df93 completed April 2, 2026, 1:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b5f15c6c8190b52924d6f86d63e0 completed April 5, 2026, 7:20 p.m.
Created at: March 30, 2026, 8:51 p.m.