Triple

T18379775
Position Surface form Disambiguated ID Type / Status
Subject Renault Samsung Motors E446411 entity
Predicate competesWith P1375 FINISHED
Object GM Korea NE NERFINISHED

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: GM Korea | Statement: [Renault Samsung Motors, competesWith, GM Korea]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: GM Korea
Context triple: [Renault Samsung Motors, competesWith, GM Korea]
  • A. GM Korea chosen
    GM Korea is the South Korean subsidiary of General Motors, engaged in the manufacturing and sale of automobiles for domestic and global markets.
  • B. BMG Korea
    BMG Korea was the South Korean branch of the global music company Bertelsmann Music Group, responsible for producing and distributing music in the Korean market.
  • C. Gumi, South Korea
    Gumi, South Korea is an industrial city in North Gyeongsang Province known as a major electronics manufacturing hub and home to several large technology companies.
  • D. GTG Entertainment
    GTG Entertainment was a television production company co-founded by influential TV executive Grant Tinker after his tenure at NBC.
  • E. GMK
    GMK is a Nigerian music producer known for crafting contemporary Afrobeats and hip-hop sounds, including work on Burna Boy’s “African Giant” era.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9f370b88190b1e5081c2c238e7f completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e51799e0f4819089e8af04888549bf completed April 19, 2026, 5:57 p.m.
Created at: April 10, 2026, 10:45 a.m.