Triple

T14588851
Position Surface form Disambiguated ID Type / Status
Subject Bruno Campos E342389 entity
Predicate notableRole P22 FINISHED
Object Prince Naveen E802190 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: Prince Naveen | Statement: [Bruno Campos, notableRole, Prince Naveen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Prince Naveen
Context triple: [Bruno Campos, notableRole, Prince Naveen]
  • A. Prince Naveen chosen
    Prince Naveen is the charming, carefree prince from Disney’s "The Princess and the Frog," known for his transformation into a frog and eventual romance with Tiana.
  • B. Prince Sameer
    Prince Sameer is one of the children of Tanzanian singer and songwriter Ali Kiba.
  • C. Prince Mahesh
    Prince Mahesh is the popular nickname of Mahesh Babu, a leading Telugu film actor and producer known for his work in South Indian cinema.
  • D. Prince Nanda
    Prince Nanda was a half-brother of the Buddha and an early Buddhist monk known for his initial attachment to worldly pleasures before attaining spiritual realization.
  • E. Prince Naseem
    Prince Naseem is the ring name of Naseem Hamed, a flamboyant British former professional boxer renowned for his explosive knockout power and unorthodox style in the featherweight division.
  • 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_69d822ddc0f081909cd8163c7de298cd completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb4237d88819097f3f9a40f5be152 completed April 14, 2026, 9:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdd5c79c488190a134d2b0435e899e completed May 8, 2026, 12:23 p.m.
Created at: April 10, 2026, 1:24 a.m.