Triple

T12877531
Position Surface form Disambiguated ID Type / Status
Subject Ugly Betty E308006 entity
Predicate executiveProducer P7225 FINISHED
Object Silvio Horta E1010418 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: Silvio Horta | Statement: [Ugly Betty, executiveProducer, Silvio Horta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Silvio Horta
Context triple: [Ugly Betty, executiveProducer, Silvio Horta]
  • A. Silvio Horta chosen
    Silvio Horta was an American television writer and producer best known for developing and showrunning the hit dramedy series "Ugly Betty."
  • B. Luciano Cardoso
    Luciano Cardoso is one of the children of former Brazilian president and sociologist Fernando Henrique Cardoso.
  • C. Ernesto Melo Antunes
    Ernesto Melo Antunes was a Portuguese military officer, intellectual, and politician who played a key ideological and diplomatic role in the Carnation Revolution and the country’s transition to democracy.
  • D. Costa Pinheiro
    Costa Pinheiro was a Portuguese artist known for his contributions to contemporary painting and public art installations.
  • E. Tulio Monteiro
    Tulio Monteiro is a character in the animated film "Rio," known as the kind-hearted ornithologist who helps protect rare birds.
  • 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_69d7bdf69bc48190af6c2621f28ca351 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d970fa8474819086a8af3c90f3ca84 completed April 10, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6b8ccee708190bb4caa604386e3a3 completed May 3, 2026, 2:54 a.m.
Created at: April 9, 2026, 5:38 p.m.