Triple

T965202
Position Surface form Disambiguated ID Type / Status
Subject Dubai Frame E20822 entity
Predicate architect P184 FINISHED
Object Fernando Donis E129320 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: Fernando Donis | Statement: [Dubai Frame, architect, Fernando Donis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fernando Donis
Context triple: [Dubai Frame, architect, Fernando Donis]
  • A. Fernando Donis chosen
    Fernando Donis is a Mexican architect known for designing prominent landmark structures, including the iconic Dubai Frame.
  • B. Claudio Fogolin
    Claudio Fogolin was an Italian entrepreneur and automotive pioneer best known as one of the founders behind the historic car manufacturer Lancia.
  • C. Sergio Chiamparino
    Sergio Chiamparino is an Italian politician best known for serving as Mayor of Turin and President of the Piedmont region.
  • D. Alberto Cavos
    Alberto Cavos was a 19th-century Russian-Italian architect best known for designing and reconstructing major imperial theaters in Russia, including the Bolshoi Theatre in Moscow and the Mariinsky Theatre in Saint Petersburg.
  • E. Alberto Colantuoni
    Alberto Colantuoni was an Italian literary figure best known for establishing the prestigious Viareggio Prize for literature.
  • 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_69a493b33d2c81909c52c369d3ca8436 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b431d61481908b53490e99670363 completed March 1, 2026, 9:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac5e9b74e481908c7d8256bd180d73 completed March 7, 2026, 5:21 p.m.
Created at: March 1, 2026, 7:40 p.m.