Triple

T23461190
Position Surface form Disambiguated ID Type / Status
Subject Lauren Holly E568977 entity
Predicate notableWork P4 FINISHED
Object Beautiful Girls NE NERFINISHED

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: Beautiful Girls | Statement: [Lauren Holly, notableWork, Beautiful Girls]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beautiful Girls
Context triple: [Lauren Holly, notableWork, Beautiful Girls]
  • A. Beautiful Girls chosen
    Beautiful Girls is a 1996 ensemble romantic dramedy film about a group of small-town friends reuniting for a high school reunion and confronting their stalled lives and relationships.
  • B. Beautiful Girls
    "Beautiful Girls" is a 2007 reggae-influenced pop song by Sean Kingston that became a worldwide hit known for its catchy melody and doo-wop-inspired hook.
  • C. These Girls
    "These Girls" is a song by American singer-songwriter Ryan Adams from his 2007 album *Easy Tiger*.
  • D. Pretty Girls
    "Pretty Girls" is a pop-rock song by Swedish duo Roxette from their 1988 album *Look Sharp!*, known for its catchy melody and upbeat production.
  • E. The Girls
    "The Girls" is an electro-pop single by Scottish DJ and producer Calvin Harris, known for its catchy synth-driven sound and playful lyrics about his attraction to different types of women.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e2458ebd808190b3298163132cfb0b completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1a69bc200819096ed2baf25cdee4f completed April 29, 2026, 6:35 a.m.
Created at: April 17, 2026, 5:53 p.m.