Triple

T21236483
Position Surface form Disambiguated ID Type / Status
Subject Jan Uddenfeldt E523355 entity
Predicate employer P7 FINISHED
Object Ericsson NE NERFINISHED

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: Ericsson | Statement: [Jan Uddenfeldt, employer, Ericsson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ericsson
Context triple: [Jan Uddenfeldt, employer, Ericsson]
  • A. Ericsson chosen
    Ericsson is a Swedish multinational telecommunications company known for providing mobile network infrastructure, services, and software to operators worldwide.
  • B. Nokia
    Nokia is a Finnish multinational telecommunications and consumer electronics company best known for its historic leadership in mobile phones and its current focus on network infrastructure and 5G technologies.
  • C. Nokia
    Nokia is a Finnish town in the Pirkanmaa region, historically known for its industrial roots and as the namesake of the multinational telecommunications company Nokia Corporation.
  • D. Alcatel
    Alcatel is a multinational telecommunications equipment and networking company known for providing infrastructure, mobile, and broadband solutions worldwide.
  • E. Telia Company
    Telia Company is a major Nordic and Baltic telecommunications provider offering mobile, broadband, and TV services across multiple countries.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b513b89c81908b27147e91368db2 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e735202c7481909c642ddaafb40671 completed April 21, 2026, 8:28 a.m.
Created at: April 16, 2026, 3:46 p.m.