Triple

T15087895
Position Surface form Disambiguated ID Type / Status
Subject Dirk Meyer E360329 entity
Predicate employer P7 FINISHED
Object Motorola E38949 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: Motorola | Statement: [Dirk Meyer, employer, Motorola]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Motorola
Context triple: [Dirk Meyer, employer, Motorola]
  • A. Motorola chosen
    Motorola is an American telecommunications and semiconductor company best known for pioneering mobile phones and designing influential microprocessors like the 68000 family.
  • B. Motorola Mobility
    Motorola Mobility is a consumer electronics company best known for designing and manufacturing Android smartphones and smartwatches, including devices running Google's Wear OS platform.
  • C. 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.
  • D. Alcatel
    Alcatel is a multinational telecommunications equipment and networking company known for providing infrastructure, mobile, and broadband solutions worldwide.
  • E. HTC
    HTC is a Taiwanese consumer electronics company best known for manufacturing smartphones and other mobile devices, including early Android and Windows-based phones.
  • 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_69d85a035aa88190b52a139d3a1b7b6d completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00276d1608190bc310d5b86ecd1d5 completed April 15, 2026, 9:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69feae1ba4208190b1e8c55668a1b422 completed May 9, 2026, 3:46 a.m.
Created at: April 10, 2026, 3:03 a.m.