Triple

T17097388
Position Surface form Disambiguated ID Type / Status
Subject LG Group E414882 entity
Predicate competesWith P1375 FINISHED
Object SK Group E605533 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: SK Group | Statement: [LG Group, competesWith, SK Group]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SK Group
Context triple: [LG Group, competesWith, SK Group]
  • A. SK Group chosen
    SK Group is one of South Korea’s largest conglomerates, with diversified businesses spanning energy, telecommunications, semiconductors, and chemicals.
  • B. Hanwha Group
    Hanwha Group is a major South Korean conglomerate with diversified businesses spanning chemicals, energy, defense, finance, and construction.
  • C. KT Corporation
    KT Corporation is one of South Korea’s largest telecommunications companies, providing mobile, internet, and media services nationwide.
  • D. Lotte Group
    Lotte Group is a major South Korean-Japanese multinational conglomerate with diverse businesses spanning food, retail, tourism, chemicals, and entertainment.
  • E. Samsung Group
    Samsung Group is a South Korean multinational conglomerate best known globally for its consumer electronics, particularly smartphones, tablets, and televisions, as well as its significant presence in semiconductors and other industries.
  • 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_69d886cfc8e88190b05ba466edd35591 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dbfe92988190aa066745ca9791d5 completed April 18, 2026, 7:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a012eedfd7c8190b267dedd403f5f2b completed May 11, 2026, 1:20 a.m.
Created at: April 10, 2026, 5:35 a.m.