Triple

T16684080
Position Surface form Disambiguated ID Type / Status
Subject Joaquín Turina E405413 entity
Predicate familyName P18 FINISHED
Object Turina
Turina is a Spanish surname most notably associated with composer Joaquín Turina, a key figure in early 20th-century Spanish classical music.
 E1228304 NE FINISHED

How this triple was built (4 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: Turina | Statement: [Joaquín Turina, familyName, Turina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Turina
Context triple: [Joaquín Turina, familyName, Turina]
  • A. Tulskaya
    Tulskaya is a Moscow Metro station on the Serpukhovsko–Timiryazevskaya Line serving the Tulskaya Square area in southern Moscow.
  • B. Swerdlov
    Swerdlov is an alternative transliteration of the Russian surname "Sverdlov," most notably associated with Bolshevik revolutionary leader Yakov Sverdlov.
  • C. Rechitsa
    Rechitsa is a historic town in southeastern Belarus, situated on the Dnieper River and known as one of the country’s oldest settlements.
  • D. Zarechny
    Zarechny is a small Russian city in Sverdlovsk Oblast known for its role in the region’s industrial and energy sectors.
  • E. Trubnaya
    Trubnaya is a Moscow Metro station located in the city center, known for its deep-level construction and transfer connection with Tsvetnoy Bulvar station.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Turina
Triple: [Joaquín Turina, familyName, Turina]
Generated description
Turina is a Spanish surname most notably associated with composer Joaquín Turina, a key figure in early 20th-century Spanish classical music.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Turina
Target entity description: Turina is a Spanish surname most notably associated with composer Joaquín Turina, a key figure in early 20th-century Spanish classical music.
  • A. Tulskaya
    Tulskaya is a Moscow Metro station on the Serpukhovsko–Timiryazevskaya Line serving the Tulskaya Square area in southern Moscow.
  • B. Swerdlov
    Swerdlov is an alternative transliteration of the Russian surname "Sverdlov," most notably associated with Bolshevik revolutionary leader Yakov Sverdlov.
  • C. Rechitsa
    Rechitsa is a historic town in southeastern Belarus, situated on the Dnieper River and known as one of the country’s oldest settlements.
  • D. Zarechny
    Zarechny is a small Russian city in Sverdlovsk Oblast known for its role in the region’s industrial and energy sectors.
  • E. Trubnaya
    Trubnaya is a Moscow Metro station located in the city center, known for its deep-level construction and transfer connection with Tsvetnoy Bulvar station.
  • F. None of above. chosen

Provenance (5 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_69d8838c28748190b3f5967c743940ab completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e37d71a66881908c8d06cc074fdf29 completed April 18, 2026, 12:47 p.m.
NED1 Entity disambiguation (via context triple) batch_6a008a422f8c8190873fd7089df8fbd4 completed May 10, 2026, 1:38 p.m.
NEDg Description generation batch_6a008b41a1648190bd1c2268c8a80ee2 completed May 10, 2026, 1:42 p.m.
NED2 Entity disambiguation (via description) batch_6a008c2bcac48190801ba34fde104a8a completed May 10, 2026, 1:46 p.m.
Created at: April 10, 2026, 5:19 a.m.