Triple

T2790630
Position Surface form Disambiguated ID Type / Status
Subject ZTE E61920 entity
Predicate competesWith P1375 FINISHED
Object Huawei E61521 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: Huawei | Statement: [ZTE, competesWith, Huawei]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Huawei
Context triple: [ZTE, competesWith, Huawei]
  • A. Huawei chosen
    Huawei is a major Chinese multinational technology company best known globally for its telecommunications equipment, smartphones, and role in 5G network infrastructure.
  • B. ZTE
    ZTE is a major Chinese telecommunications and technology company known for manufacturing network equipment and smartphones and competing globally with firms like Nokia and Huawei.
  • 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. Ericsson
    Ericsson is a Swedish multinational telecommunications company known for providing mobile network infrastructure, services, and software to operators worldwide.
  • E. Motorola
    Motorola is an American telecommunications and semiconductor company best known for pioneering mobile phones and designing influential microprocessors like the 68000 family.
  • 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_69ab4b7f51d881908768300ebd2fbdae completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abddceb9d88190961e30d521a21552 completed March 7, 2026, 8:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc65c1f848190b6efeefb64a3e131 completed March 10, 2026, 7:21 a.m.
Created at: March 6, 2026, 9:58 p.m.