Triple

T10718438
Position Surface form Disambiguated ID Type / Status
Subject Teddy Geiger E252747 entity
Predicate notableWork P4 FINISHED
Object You Ruin Me E546799 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: You Ruin Me | Statement: [Teddy Geiger, notableWork, You Ruin Me]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: You Ruin Me
Context triple: [Teddy Geiger, notableWork, You Ruin Me]
  • A. You Ruin Me
    "You Ruin Me" is a piano-driven pop ballad by Australian duo The Veronicas, known for its raw, emotional vocals and themes of heartbreak and betrayal.
  • B. Ruin My Life
    "Ruin My Life" is a pop song by Swedish singer Zara Larsson, known for its emotive lyrics about toxic relationships and its commercial success on international charts.
  • C. Destroy Me
    "Destroy Me" is a pop single by American singer Rebecca Black that showcases her evolution from viral teen sensation to more mature recording artist.
  • D. Bad to Me
    "Bad to Me" is a 1963 pop song written by John Lennon and Paul McCartney that became a hit single for Billy J. Kramer and the Dakotas.
  • E. You Kill Me
    You Kill Me is a 2007 dark comedy crime film about an alcoholic hitman trying to reform his life, directed by John Dahl and starring Ben Kingsley and Téa Leoni.
  • 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_69d6aa5d8be481909a43218b2bfdbe95 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6ff36558c81908682adbe7b5dce05 completed April 9, 2026, 1:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69dbb71dd6f88190beb99ca75914fb09 completed April 12, 2026, 3:15 p.m.
Created at: April 8, 2026, 9:13 p.m.