Triple

T12052671
Position Surface form Disambiguated ID Type / Status
Subject Célestine E286955 entity
Predicate hasRelatedName P3889 FINISHED
Object Celestina E177603 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: Celestina | Statement: [Célestine, hasRelatedName, Celestina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Celestina
Context triple: [Célestine, hasRelatedName, Celestina]
  • A. La Celestina chosen
    La Celestina is a seminal late 15th-century Spanish tragicomedy, often considered a precursor to the modern novel and a cornerstone of Spanish Renaissance literature.
  • B. La Rosaura
    La Rosaura is an opera by Italian Baroque composer Alessandro Scarlatti, exemplifying his influential contribution to early 18th-century opera.
  • C. La Jara
    La Jara is a small town in southern Colorado that serves as the primary population and commercial center of Conejos County.
  • D. Fando y Lis
    Fando y Lis is a 1968 surrealist film by Alejandro Jodorowsky that follows a young couple’s bizarre, allegorical journey through a post-apocalyptic landscape.
  • E. Arcipreste de Hita
    Arcipreste de Hita is the pen name of medieval Spanish writer Juan Ruiz, best known as the author of the satirical and didactic poem "Libro de buen amor."
  • 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_69d6ab4780948190bdb9f7620c2ac27e completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d90423b22081908fba82fbc6b40eb5 completed April 10, 2026, 2:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69f49ddde6548190adae2a889ec5c72b completed May 1, 2026, 12:34 p.m.
Created at: April 8, 2026, 9:47 p.m.