Triple

T15122018
Position Surface form Disambiguated ID Type / Status
Subject My World E361192 entity
Predicate mainSingle P16320 FINISHED
Object One Less Lonely Girl E361176 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: One Less Lonely Girl | Statement: [My World, mainSingle, One Less Lonely Girl]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: One Less Lonely Girl
Context triple: [My World, mainSingle, One Less Lonely Girl]
  • A. One Less Lonely Girl chosen
    "One Less Lonely Girl" is a pop-R&B song by Canadian singer Justin Bieber from his debut EP "My World," known for its romantic theme and early role in launching his career.
  • B. Every Girl
    "Every Girl" is a 2009 hip hop single by Young Money Entertainment featuring several of the label’s artists, known for its catchy hook and sexually explicit lyrics.
  • C. Your Girl
    "Your Girl" is a song by Mariah Carey from her 2005 album *The Emancipation of Mimi*.
  • D. Lonely Girl
    "Lonely Girl" is a song by American singer-songwriter and producer Linda Perry, known for its introspective lyrics and emotive vocal style.
  • E. Not One Less
    Not One Less is a 1999 Chinese drama film directed by Zhang Yimou that follows a young substitute teacher’s struggle to keep her impoverished rural students from dropping out of school.
  • 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_69d85a06450081909c5a14ea9851a15e completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0059f69a881909929a037a0eef702 completed April 15, 2026, 9:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69fed321d6108190a30b32f176e6d4dc completed May 9, 2026, 6:24 a.m.
Created at: April 10, 2026, 3:06 a.m.