Triple

T13830667
Position Surface form Disambiguated ID Type / Status
Subject Cheat Codes E332386 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: [Cheat Codes, recordLabel, BMG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: BMG
Context triple: [Cheat Codes, 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
    BMG is the IATA airport code for Monroe County Airport serving Bloomington, Indiana.
  • D. BMG Interactive
    BMG Interactive was a mid-1990s video game publishing and distribution division of BMG Entertainment, known for releasing several notable console and PC titles before being absorbed into other companies.
  • E. 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.
  • 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_69d81c5ae7c88190b0dd41bdafeb5999 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de0299334481908c2b271eaf06e4b7 completed April 14, 2026, 9:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7b8ebf2608190b1071ee6967fa8d3 completed May 3, 2026, 9:06 p.m.
Created at: April 9, 2026, 10:13 p.m.