Triple

T7531772
Position Surface form Disambiguated ID Type / Status
Subject I Am Greta E178039 entity
Predicate composer P1361 FINISHED
Object Jon Ekstrand E34988 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: Jon Ekstrand | Statement: [I Am Greta, composer, Jon Ekstrand]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jon Ekstrand
Context triple: [I Am Greta, composer, Jon Ekstrand]
  • A. Jon Ekstrand chosen
    Jon Ekstrand is a Swedish film composer and sound designer known for his atmospheric scores for documentaries and feature films, including collaborations with director Daniel Espinosa.
  • B. Daniel Nannskog
    Daniel Nannskog is a retired Swedish striker best known for his prolific goal-scoring spell at Norwegian club Stabæk Fotball and later work as a football pundit.
  • C. Greg Eklund
    Greg Eklund is an American drummer best known for his work with the alternative rock band Everclear.
  • D. Erik Edlund
    Erik Edlund was a Swedish physicist and academic who mentored future Nobel laureate Svante Arrhenius and contributed to 19th-century physical science education in Sweden.
  • E. Marcus Fjellström
    Marcus Fjellström was a Swedish composer known for his darkly atmospheric, experimental works that blended contemporary classical music with electronic and cinematic elements.
  • 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_69c69f2acdbc8190b5a8320168c1d0ba completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f84753fc81908bee2013004ef5fb completed March 27, 2026, 9:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69c84640cd2c819094d8f72d82c71e67 completed March 28, 2026, 9:21 p.m.
Created at: March 27, 2026, 3:47 p.m.