Triple

T2790653
Position Surface form Disambiguated ID Type / Status
Subject ZTE E61920 entity
Predicate hasSubsidiary P254 FINISHED
Object ZTE India E61920 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: ZTE India | Statement: [ZTE, hasSubsidiary, ZTE India]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ZTE India
Context triple: [ZTE, hasSubsidiary, ZTE India]
  • A. ZTE chosen
    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.
  • B. Huawei
    Huawei is a major Chinese multinational technology company best known globally for its telecommunications equipment, smartphones, and role in 5G network infrastructure.
  • C. LG Mobile Communications
    LG Mobile Communications was the mobile phone and smartphone division of LG Electronics, known for producing a wide range of feature phones and Android devices before exiting the smartphone market.
  • D. Alcatel
    Alcatel is a multinational telecommunications equipment and networking company known for providing infrastructure, mobile, and broadband solutions worldwide.
  • E. Larsen & Toubro
    Larsen & Toubro is a major Indian multinational conglomerate known for its leadership in engineering, construction, manufacturing, and technology services.
  • 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_69afce8e2c488190a63332f36803c295 completed March 10, 2026, 7:55 a.m.
Created at: March 6, 2026, 9:58 p.m.