Triple

T22255413
Position Surface form Disambiguated ID Type / Status
Subject Echo & the Bunnymen E550082 entity
Predicate hasSong P20452 FINISHED
Object Lips Like Sugar 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: Lips Like Sugar | Statement: [Echo & the Bunnymen, hasSong, Lips Like Sugar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lips Like Sugar
Context triple: [Echo & the Bunnymen, hasSong, Lips Like Sugar]
  • A. Lips Like Sugar chosen
    "Lips Like Sugar" is a 1987 alternative rock song by Echo & the Bunnymen, known for its atmospheric sound and enduring popularity as one of the band's signature tracks.
  • B. Sugar Lips
    "Sugar Lips" is a popular jazz trumpet tune by Al Hirt that became one of his signature hits in the 1960s.
  • C. Hot Lips
    Hot Lips is the nickname of Major Margaret Houlihan, the strict yet evolving head nurse character from the M*A*S*H franchise.
  • D. Hot Lips
    Hot Lips is the nickname of Oran "Hot Lips" Page, an influential American jazz trumpeter and vocalist known for his powerful playing and contributions to the swing era.
  • E. Milk Like Sugar
    Milk Like Sugar is a contemporary stage play that explores the aspirations and harsh realities of teenage girls who make a pact to become pregnant in pursuit of a better life.
  • 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_69e11e42adb8819087714772ea606709 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f138c1d70881908df47b0f818c0022 completed April 28, 2026, 10:46 p.m.
Created at: April 16, 2026, 8:39 p.m.