Triple

T37932167
Position Surface form Disambiguated ID Type / Status
Subject Ian Bostridge E946247 entity
Predicate specialization P466 FINISHED
Object German lieder
German lieder are art songs from the German-speaking classical tradition, typically for solo voice and piano, that set German poetry to music with a strong emphasis on expressive text setting and intimate musical interpretation.
E2248867 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: German lieder | Statement: [Ian Bostridge, specialization, German lieder]
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: German lieder
Triple: [Ian Bostridge, specialization, German lieder]
Generated description
German lieder are art songs from the German-speaking classical tradition, typically for solo voice and piano, that set German poetry to music with a strong emphasis on expressive text setting and intimate musical interpretation.

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_69f76ef3b7248190892fb9706423be7c completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fbbd97e9708190bcebde2e8092815c completed May 6, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a410cdacd348190b3ff8a69076071df completed June 28, 2026, noon
NEDg Description generation batch_6a410dc8b4f881909d93f5de3c979c35 completed June 28, 2026, 12:04 p.m.
NED2 Entity disambiguation (via description) batch_6a410e50a2d0819092ce4ff0863ecbc2 completed June 28, 2026, 12:06 p.m.
Created at: May 3, 2026, 4:20 p.m.