Triple

T2211836
Position Surface form Disambiguated ID Type / Status
Subject Viktor Knavs E50933 entity
Predicate residence P75 FINISHED
Object Sevnica
Sevnica is a small town in central Slovenia known as the childhood home of former U.S. First Lady Melania Trump.
 E246457 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: Sevnica | Statement: [Viktor Knavs, residence, Sevnica]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sevnica
Context triple: [Viktor Knavs, residence, Sevnica]
  • A. Gospić
    Gospić is a town in the Lika region of Croatia, known as the administrative center of Lika-Senj County and for its association with the birthplace of inventor Nikola Tesla in nearby Smiljan.
  • B. Kladno
    Kladno is an industrial city in the Czech Republic known historically for coal mining and steel production.
  • C. Ptuj
    Ptuj is one of Slovenia’s oldest towns, renowned for its well-preserved medieval architecture and rich cultural heritage along the Drava River.
  • D. Maribor
    Maribor is Slovenia’s second-largest city, known for its historic old town, wine culture, and the world’s oldest grapevine.
  • E. Radeče
    Radeče is a small town in central Slovenia, situated on the banks of the Sava River and known for its paper industry and scenic surroundings.
  • 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: Sevnica
Triple: [Viktor Knavs, residence, Sevnica]
Generated description
Sevnica is a small town in central Slovenia known as the childhood home of former U.S. First Lady Melania Trump.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sevnica
Target entity description: Sevnica is a small town in central Slovenia known as the childhood home of former U.S. First Lady Melania Trump.
  • A. Gospić
    Gospić is a town in the Lika region of Croatia, known as the administrative center of Lika-Senj County and for its association with the birthplace of inventor Nikola Tesla in nearby Smiljan.
  • B. Kladno
    Kladno is an industrial city in the Czech Republic known historically for coal mining and steel production.
  • C. Ptuj
    Ptuj is one of Slovenia’s oldest towns, renowned for its well-preserved medieval architecture and rich cultural heritage along the Drava River.
  • D. Maribor
    Maribor is Slovenia’s second-largest city, known for its historic old town, wine culture, and the world’s oldest grapevine.
  • E. Radeče
    Radeče is a small town in central Slovenia, situated on the banks of the Sava River and known for its paper industry and scenic surroundings.
  • 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_69a88b06709c8190978fb2418470d1b6 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abbfecea6c8190b762bbfda8490e31 completed March 7, 2026, 6:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae6af84b708190ac3170a343eb107f completed March 9, 2026, 6:38 a.m.
NEDg Description generation batch_69ae6b31eccc81908fbcc80f72e65df8 completed March 9, 2026, 6:39 a.m.
NED2 Entity disambiguation (via description) batch_69ae6bd2856c81909033efe74039c258 completed March 9, 2026, 6:42 a.m.
Created at: March 4, 2026, 7:46 p.m.