Triple

T12441683
Position Surface form Disambiguated ID Type / Status
Subject Hjördis Genberg E297286 entity
Predicate familyName P18 FINISHED
Object Genberg
Genberg is a Swedish surname most notably associated with individuals such as Hjördis Genberg, a mid-20th-century Swedish model and actress.
 E984354 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: Genberg | Statement: [Hjördis Genberg, familyName, Genberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Genberg
Context triple: [Hjördis Genberg, familyName, Genberg]
  • A. Geva
    Geva is a surname most notably associated with Tamara Geva, a Russian-American actress, dancer, and choreographer.
  • B. Löwenberg
    Löwenberg is a town in Germany known for its cultural and municipal partnership as a twin town of Weilburg.
  • C. Grünberg
    Grünberg is a small German town in the state of Hesse, known for its historic half-timbered old town and traditional regional festivals.
  • D. Reisenberg
    Reisenberg is a small municipality in Lower Austria’s Baden District, known for its rural character and proximity to Vienna.
  • E. Bordenau
    Bordenau is a small village in Lower Saxony, Germany, historically noted as the birthplace of Prussian military reformer Gerhard von Scharnhorst.
  • 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: Genberg
Triple: [Hjördis Genberg, familyName, Genberg]
Generated description
Genberg is a Swedish surname most notably associated with individuals such as Hjördis Genberg, a mid-20th-century Swedish model and actress.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Genberg
Target entity description: Genberg is a Swedish surname most notably associated with individuals such as Hjördis Genberg, a mid-20th-century Swedish model and actress.
  • A. Geva
    Geva is a surname most notably associated with Tamara Geva, a Russian-American actress, dancer, and choreographer.
  • B. Löwenberg
    Löwenberg is a town in Germany known for its cultural and municipal partnership as a twin town of Weilburg.
  • C. Grünberg
    Grünberg is a small German town in the state of Hesse, known for its historic half-timbered old town and traditional regional festivals.
  • D. Reisenberg
    Reisenberg is a small municipality in Lower Austria’s Baden District, known for its rural character and proximity to Vienna.
  • E. Bordenau
    Bordenau is a small village in Lower Saxony, Germany, historically noted as the birthplace of Prussian military reformer Gerhard von Scharnhorst.
  • 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_69d6ada166c48190b902972cd2408fa3 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94d8ecb6c8190a19cbf9de31cabbd completed April 10, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63f0ea1e48190a11cf94797290156 completed May 2, 2026, 6:14 p.m.
NEDg Description generation batch_69f64010a1348190afaf7b95b8f146b5 completed May 2, 2026, 6:18 p.m.
NED2 Entity disambiguation (via description) batch_69f640c33d948190ad8f9885f90786d7 completed May 2, 2026, 6:21 p.m.
Created at: April 8, 2026, 9:55 p.m.