Triple

T20434612
Position Surface form Disambiguated ID Type / Status
Subject Live in Paris E501219 entity
Predicate hasTrack P3284 FINISHED
Object Let’s Fall in Love 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: Let’s Fall in Love | Statement: [Live in Paris, hasTrack, Let’s Fall in Love]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Let’s Fall in Love
Context triple: [Live in Paris, hasTrack, Let’s Fall in Love]
  • A. Let’s Fall in Love chosen
    "Let’s Fall in Love" is a track featured on the album *ArtScience* by the jazz fusion collective Snarky Puppy.
  • B. Falling in Love
    "Falling in Love" is a 1984 romantic drama film starring Robert De Niro and Meryl Streep as two married strangers who develop a deep emotional connection after a chance meeting.
  • C. Falling in Love
    "Falling in Love" is a soulful love song by Sam Cooke featured on his 1964 album *Ain't That Good News*.
  • D. Let’s Do It, Let’s Fall in Love
    "Let’s Do It, Let’s Fall in Love" is a classic popular song from the late 1920s, known for its witty, innuendo-laden lyrics and sophisticated wordplay.
  • E. Falling in Love with Love
    "Falling in Love with Love" is a popular show tune by composer Richard Rodgers and lyricist Lorenz Hart, introduced in the 1938 musical "The Boys from Syracuse."
  • 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_69e0b4ab3cfc8190ac9bf32e932316b1 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e685ecf7ac81908e9c2ef4348fb281 completed April 20, 2026, 8 p.m.
Created at: April 16, 2026, 11:31 a.m.