Triple

T3802072
Position Surface form Disambiguated ID Type / Status
Subject IM Motors E91710 entity
Predicate competitor P1375 FINISHED
Object Xpeng Motors E323035 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: Xpeng Motors | Statement: [IM Motors, competitor, Xpeng Motors]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xpeng Motors
Context triple: [IM Motors, competitor, Xpeng Motors]
  • A. XPeng chosen
    XPeng is a Chinese electric vehicle manufacturer known for its smart, tech-focused cars and advanced driver-assistance systems.
  • B. BYD Auto
    BYD Auto is a major Chinese automobile manufacturer best known for its electric and hybrid vehicles and rapid global expansion in the new energy vehicle market.
  • C. Zoomlion
    Zoomlion is a major Chinese manufacturer of construction machinery and sanitation equipment, known as one of the largest heavy equipment companies in China.
  • D. Lucid Group
    Lucid Group is an American electric vehicle manufacturer best known for its luxury, high-performance EVs that compete with brands like Tesla and Mercedes-Benz.
  • E. Evergrande New Energy Vehicle
    Evergrande New Energy Vehicle is a Chinese electric vehicle manufacturer established by the Evergrande Group as part of its diversification into the new energy automotive sector.
  • 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_69aed96354f48190a768966d6bd19b04 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aee7b998c08190b252178cd7436951 completed March 9, 2026, 3:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4f066e30481909e5baa630f3539e4 completed March 14, 2026, 5:21 a.m.
Created at: March 9, 2026, 3:15 p.m.