Triple

T14850433
Position Surface form Disambiguated ID Type / Status
Subject Ashes E349210 entity
Predicate originalLanguageTitle P13516 FINISHED
Object Aske E479553 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: Aske | Statement: [Ashes, originalLanguageTitle, Aske]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aske
Context triple: [Ashes, originalLanguageTitle, Aske]
  • A. Aske chosen
    Aske is a surname of Scandinavian origin borne by various individuals, including those with the given name Ellen.
  • B. Asker
    Asker is a municipality in Viken county, Norway, known for its coastal location near Oslo and its mix of residential areas, cultural sites, and natural landscapes.
  • C. Askeran
    Askeran is a town in the disputed Nagorno-Karabakh region of the South Caucasus, historically known for its strategic location and fortress.
  • D. Andselv
    Andselv is a small Norwegian village located in the Troms region, known for its position along the Andselva river and proximity to Bardufoss.
  • E. Askim
    Askim is a town in southeastern Norway that serves as one of the locations for Østfold University College’s campuses.
  • 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_69d822ed7e1881909b90fca143ad7e34 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded43eee188190bf24dc475b3abe28 completed April 14, 2026, 11:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe6504ac6081908074231cf628fd39 completed May 8, 2026, 10:34 p.m.
Created at: April 10, 2026, 1:54 a.m.