Triple

T5754523
Position Surface form Disambiguated ID Type / Status
Subject The Main Ingredient E126931 entity
Predicate recordLabel P1500 FINISHED
Object BMG E135662 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: BMG | Statement: [The Main Ingredient, recordLabel, BMG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: BMG
Context triple: [The Main Ingredient, recordLabel, BMG]
  • A. BMG
    BMG is the commonly used abbreviation for Germany’s Federal Ministry of Health.
  • B. BMG chosen
    BMG is a major global music company known for its music publishing and recorded music services, representing a wide range of international artists and catalogs.
  • C. BMG Classics
    BMG Classics was the classical music division and record label of Bertelsmann Music Group, known for releasing and distributing classical recordings by major orchestras, conductors, and soloists.
  • D. BMG India
    BMG India was the Indian division of the global music company Bertelsmann Music Group, responsible for producing and distributing music in the Indian market.
  • E. 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.
  • 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_69c00832aedc81909899801b141fa3b4 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02904dcf481909e4340a64ee1034e completed March 22, 2026, 5:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07e434aa88190b56e721c002d31b8 completed March 22, 2026, 11:41 p.m.
Created at: March 22, 2026, 3:49 p.m.