Triple

T10097816
Position Surface form Disambiguated ID Type / Status
Subject Oh Shenandoah E215916 entity
Predicate hasRecordingBy P1152 FINISHED
Object Sissel Kyrkjebø
Sissel Kyrkjebø is a Norwegian soprano renowned for her crystal-clear voice and wide-ranging repertoire spanning classical, folk, and pop music.
 E840004 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: Sissel Kyrkjebø | Statement: [Oh Shenandoah, hasRecordingBy, Sissel Kyrkjebø]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sissel Kyrkjebø
Context triple: [Oh Shenandoah, hasRecordingBy, Sissel Kyrkjebø]
  • A. Ane Brun
    Ane Brun is a Norwegian singer-songwriter known for her introspective folk-pop music and distinctive, emotive vocal style.
  • B. Sandi Sissel
    Sandi Sissel is an American cinematographer and documentary filmmaker known for her work on both narrative features and non-fiction films.
  • C. Maren Svarstad
    Maren Svarstad was a daughter of the Norwegian Nobel Prize–winning author Sigrid Undset.
  • D. Karin Boye
    Karin Boye was a prominent 20th-century Swedish poet and novelist, best known for her lyrical poetry and the dystopian novel "Kallocain."
  • E. Anna Sofie Bergen
    Anna Sofie Bergen was the mother of composer and cultural figure Alma Mahler, belonging to the milieu of late 19th-century Viennese artistic society.
  • 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: Sissel Kyrkjebø
Triple: [Oh Shenandoah, hasRecordingBy, Sissel Kyrkjebø]
Generated description
Sissel Kyrkjebø is a Norwegian soprano renowned for her crystal-clear voice and wide-ranging repertoire spanning classical, folk, and pop music.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sissel Kyrkjebø
Target entity description: Sissel Kyrkjebø is a Norwegian soprano renowned for her crystal-clear voice and wide-ranging repertoire spanning classical, folk, and pop music.
  • A. Ane Brun
    Ane Brun is a Norwegian singer-songwriter known for her introspective folk-pop music and distinctive, emotive vocal style.
  • B. Sandi Sissel
    Sandi Sissel is an American cinematographer and documentary filmmaker known for her work on both narrative features and non-fiction films.
  • C. Maren Svarstad
    Maren Svarstad was a daughter of the Norwegian Nobel Prize–winning author Sigrid Undset.
  • D. Karin Boye
    Karin Boye was a prominent 20th-century Swedish poet and novelist, best known for her lyrical poetry and the dystopian novel "Kallocain."
  • E. Anna Sofie Bergen
    Anna Sofie Bergen was the mother of composer and cultural figure Alma Mahler, belonging to the milieu of late 19th-century Viennese artistic society.
  • 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_69ca83a4947c8190823a7495dc5d96ed completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdd07c0e248190b4ab450e0b83ea0c completed April 2, 2026, 2:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b6c218b08190853b0979296e2f83 completed April 5, 2026, 7:23 p.m.
NEDg Description generation batch_69d2b790cc188190b5c16c89beaa8aca completed April 5, 2026, 7:27 p.m.
NED2 Entity disambiguation (via description) batch_69d2b81e7b948190baa417186aac284b completed April 5, 2026, 7:29 p.m.
Created at: March 30, 2026, 9:02 p.m.