Triple

T21001571
Position Surface form Disambiguated ID Type / Status
Subject Miami Sound Machine E517301 entity
Predicate hasSignatureSong P7258 FINISHED
Object Conga 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: Conga | Statement: [Miami Sound Machine, hasSignatureSong, Conga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Conga
Context triple: [Miami Sound Machine, hasSignatureSong, Conga]
  • A. Conga chosen
    "Conga" is a 1985 Latin pop-dance hit by Gloria Estefan and Miami Sound Machine that popularized Latin rhythms in mainstream American pop music.
  • B. Conga!
    "Conga!" is a lively musical number from the 1953 Broadway musical *Wonderful Town*, known for its energetic Latin-inspired rhythm and ensemble choreography.
  • C. Coramba
    Coramba is a small rural town in New South Wales, Australia, situated inland from Coffs Harbour in the Coffs Coast region.
  • D. Dor Bongo
    Dor Bongo is an alternative name for the Bongo language, a Central Sudanic language spoken primarily in South Sudan.
  • E. Ventas Rumba
    Ventas Rumba is a wide, low waterfall on the Venta River in Kuldīga, Latvia, known as one of the broadest natural waterfalls in Europe.
  • 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_69e0b5006e2881909fc2383f841740cc completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e6fc25c1f8819086bdbfd89d390f5f completed April 21, 2026, 4:25 a.m.
Created at: April 16, 2026, 1:52 p.m.