Triple

T23231164
Position Surface form Disambiguated ID Type / Status
Subject Lieutenant Thomas Glahn E581156 entity
Predicate hasRelationshipWith P2830 FINISHED
Object Edvarda NE NERFINISHED

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: Edvarda | Statement: [Lieutenant Thomas Glahn, hasRelationshipWith, Edvarda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Edvarda
Context triple: [Lieutenant Thomas Glahn, hasRelationshipWith, Edvarda]
  • A. Edvarda chosen
    Edvarda is a central fictional character in Knut Hamsun’s novel "Pan," known for her complex and tumultuous relationship with the protagonist.
  • B. Henrike
    Henrike is a feminine given name of German origin, serving as the female form of Heinrich.
  • C. Ludvika
    Ludvika is a small industrial town in central Sweden known for its engineering and manufacturing industries, particularly in the power and electrical sectors.
  • D. Dagmar
    Dagmar is a feminine given name of Germanic origin, historically associated with European nobility and still used in various countries today.
  • E. Vilhelmi
    Vilhelmi is a given name that serves as an alternative spelling of the name Vilhelm, itself a Scandinavian form of William.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e246043c48819089bae72c9a9c306c completed April 17, 2026, 2:39 p.m.
NER Named-entity recognition batch_69f19231ef908190a791b4967916a66f completed April 29, 2026, 5:08 a.m.
Created at: April 17, 2026, 4:09 p.m.