Triple

T14372021
Position Surface form Disambiguated ID Type / Status
Subject Lee Maeng-hee E356379 entity
Predicate employer P7 FINISHED
Object Samsung Group E1010375 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: Samsung Group | Statement: [Lee Maeng-hee, employer, Samsung Group]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Samsung Group
Context triple: [Lee Maeng-hee, employer, Samsung Group]
  • A. Samsung Group chosen
    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.
  • B. LG Corporation
    LG Corporation is a major South Korean multinational conglomerate with diversified businesses spanning electronics, chemicals, and telecommunications.
  • C. Hanwha Group
    Hanwha Group is a major South Korean conglomerate with diversified businesses spanning chemicals, energy, defense, finance, and construction.
  • D. SK Group
    SK Group is one of South Korea’s largest conglomerates, with diversified businesses spanning energy, telecommunications, semiconductors, and chemicals.
  • E. LG Group
    LG Group is a major South Korean multinational conglomerate known for its electronics, chemicals, and telecommunications businesses.
  • 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_69d8279163a081908aec45c0e3f1e02f completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de8fb2082c8190b42cc5f2bab4f574 completed April 14, 2026, 7:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69fde16198488190ad69eeebd09c45da completed May 8, 2026, 1:13 p.m.
Created at: April 10, 2026, 1:15 a.m.