Triple

T12345874
Position Surface form Disambiguated ID Type / Status
Subject Jo Harlow E294352 entity
Predicate employer P7 FINISHED
Object Nokia E9106 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: Nokia | Statement: [Jo Harlow, employer, Nokia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nokia
Context triple: [Jo Harlow, employer, Nokia]
  • A. Nokia chosen
    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.
  • B. Ericsson
    Ericsson is a Swedish multinational telecommunications company known for providing mobile network infrastructure, services, and software to operators worldwide.
  • C. Motorola
    Motorola is an American telecommunications and semiconductor company best known for pioneering mobile phones and designing influential microprocessors like the 68000 family.
  • D. Sony Ericsson
    Sony Ericsson was a joint venture mobile phone manufacturer formed by Japan’s Sony Corporation and Sweden’s Ericsson, known for producing feature-rich and multimedia-focused handsets in the 2000s.
  • E. Alcatel
    Alcatel is a multinational telecommunications equipment and networking company known for providing infrastructure, mobile, and broadband solutions worldwide.
  • 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_69d6ab6ccbec8190b09e2d357aa80064 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f7a4a448190aa70d66dc2f406c1 completed April 10, 2026, 6:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63eedd7a4819082f2049dcfff6401 completed May 2, 2026, 6:14 p.m.
Created at: April 8, 2026, 9:53 p.m.