Triple

T4808263
Position Surface form Disambiguated ID Type / Status
Subject Mimi Fariña E106998 entity
Predicate givenName P17 FINISHED
Object Margarita E66654 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: Margarita | Statement: [Mimi Fariña, givenName, Margarita]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Margarita
Context triple: [Mimi Fariña, givenName, Margarita]
  • A. Margarita chosen
    Margarita is a feminine given name of Spanish origin, equivalent to "Margaret" in English.
  • B. Margarita
    Margarita is a classic tequila-based cocktail typically made with lime juice and orange liqueur, often served in a salt-rimmed glass.
  • C. Tequila and Bonetti
    Tequila and Bonetti is an early-1990s American comedy-drama television series about a New York cop partnered with a talking police dog in a California beach town.
  • D. The Bottle of Anís del Mono
    The Bottle of Anís del Mono is a famous Cubist still-life painting by Juan Gris, notable for its fragmented depiction of a popular Spanish anisette bottle.
  • E. Mojito
    Mojito is a classic Cuban cocktail typically made with white rum, fresh lime juice, mint, sugar, and soda water, known for its refreshing, citrusy-minty flavor.
  • 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_69bd43f779448190b92885cb70abb6c2 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6c6a98a481909ef273d9946906a4 completed March 20, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69be4da6a9b4819083706381a57e2c73 completed March 21, 2026, 7:49 a.m.
Created at: March 20, 2026, 1:23 p.m.