Triple

T2279077
Position Surface form Disambiguated ID Type / Status
Subject Sveriges Television E51238 entity
Predicate operatesChannel P5884 FINISHED
Object SVT2
SVT2 is a Swedish public television channel known for its focus on culture, current affairs, and minority-language programming.
 E251090 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: SVT2 | Statement: [Sveriges Television, operatesChannel, SVT2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SVT2
Context triple: [Sveriges Television, operatesChannel, SVT2]
  • A. SVT
    SVT (Special Vehicle Team) is Ford Motor Company's high-performance division responsible for developing enhanced, limited-production models like the Cobra R.
  • B. VTST
    VTST is the station code for the Vermont/Sunset station on the Los Angeles Metro Rail system.
  • C. SV
    SV is the two-letter ISO 3166-1 alpha-2 country code assigned to El Salvador.
  • D. SV
    SV is the commonly used abbreviation for the Faculty of Social Sciences at the University of Oslo, encompassing disciplines such as sociology, political science, economics, and related fields.
  • E. SVC
    SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
  • 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: SVT2
Triple: [Sveriges Television, operatesChannel, SVT2]
Generated description
SVT2 is a Swedish public television channel known for its focus on culture, current affairs, and minority-language programming.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SVT2
Target entity description: SVT2 is a Swedish public television channel known for its focus on culture, current affairs, and minority-language programming.
  • A. SVT
    SVT (Special Vehicle Team) is Ford Motor Company's high-performance division responsible for developing enhanced, limited-production models like the Cobra R.
  • B. VTST
    VTST is the station code for the Vermont/Sunset station on the Los Angeles Metro Rail system.
  • C. SV
    SV is the two-letter ISO 3166-1 alpha-2 country code assigned to El Salvador.
  • D. SV
    SV is the commonly used abbreviation for the Faculty of Social Sciences at the University of Oslo, encompassing disciplines such as sociology, political science, economics, and related fields.
  • E. SVC
    SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
  • 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_69a88b08e4308190bdac9aebcca1c91a completed March 4, 2026, 7:42 p.m.
NER Named-entity recognition batch_69abc2194150819083156e4dcd45a423 completed March 7, 2026, 6:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae71e2a17081908539717619ad7187 completed March 9, 2026, 7:08 a.m.
NEDg Description generation batch_69ae75ba1a988190ba59d3ce5e5c39a8 completed March 9, 2026, 7:24 a.m.
NED2 Entity disambiguation (via description) batch_69ae76246f6c81909a15262d2c4ea975 completed March 9, 2026, 7:26 a.m.
Created at: March 4, 2026, 7:48 p.m.