Triple

T15121713
Position Surface form Disambiguated ID Type / Status
Subject Beauty and a Beat E361186 entity
Predicate writer P1360 FINISHED
Object Savan Kotecha E653108 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: Savan Kotecha | Statement: [Beauty and a Beat, writer, Savan Kotecha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Savan Kotecha
Context triple: [Beauty and a Beat, writer, Savan Kotecha]
  • A. Savan Kotecha chosen
    Savan Kotecha is an American songwriter and record producer best known for crafting numerous global pop hits for artists like One Direction, Ariana Grande, and The Weeknd.
  • B. Divyank Turakhia
    Divyank Turakhia is an Indian serial tech entrepreneur and billionaire best known for building and selling multiple internet and ad-tech companies, including those under the Directi Group.
  • C. Rohan Oza
    Rohan Oza is a marketing executive and entrepreneur best known for building and investing in major consumer brands like Vitaminwater and for his appearances as a guest investor on Shark Tank.
  • D. Karan Patel
    Karan Patel is an Indian television actor best known for his role as Raman Bhalla in the popular Hindi TV series "Yeh Hai Mohabbatein."
  • E. Dillon Mitra
    Dillon Mitra is an actor known for his role in the culinary drama film "The Hundred-Foot Journey."
  • 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_69feb7f4abd08190b47c9daff2921919 completed May 9, 2026, 4:28 a.m.
Created at: April 10, 2026, 3:06 a.m.