Triple

T1891199
Position Surface form Disambiguated ID Type / Status
Subject Mordecai Kaplan E41877 entity
Predicate placeOfBirth P1 FINISHED
Object Švenčionys
Švenčionys is a small historic town in eastern Lithuania known for its multicultural past and former Jewish community.
 E209675 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: Švenčionys | Statement: [Mordecai Kaplan, placeOfBirth, Švenčionys]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Švenčionys
Context triple: [Mordecai Kaplan, placeOfBirth, Švenčionys]
  • A. Alytus
    Alytus is a city in southern Lithuania known as a regional cultural and economic center on the banks of the Nemunas River.
  • B. Panevėžys
    Panevėžys is a major city in northern Lithuania known as an important regional industrial and cultural center.
  • C. Kaunas
    Kaunas is the second-largest city in Lithuania, known as a historic cultural and academic center located at the confluence of the Nemunas and Neris rivers.
  • D. Klaipėda
    Klaipėda is a Lithuanian port city on the Baltic Sea known as the country’s main maritime gateway and a key regional transport and industrial hub.
  • E. Vilnius
    Vilnius is the capital and largest city of Lithuania, known for its well-preserved medieval Old Town and rich cultural and historical heritage.
  • 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: Švenčionys
Triple: [Mordecai Kaplan, placeOfBirth, Švenčionys]
Generated description
Švenčionys is a small historic town in eastern Lithuania known for its multicultural past and former Jewish community.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Švenčionys
Target entity description: Švenčionys is a small historic town in eastern Lithuania known for its multicultural past and former Jewish community.
  • A. Alytus
    Alytus is a city in southern Lithuania known as a regional cultural and economic center on the banks of the Nemunas River.
  • B. Panevėžys
    Panevėžys is a major city in northern Lithuania known as an important regional industrial and cultural center.
  • C. Kaunas
    Kaunas is the second-largest city in Lithuania, known as a historic cultural and academic center located at the confluence of the Nemunas and Neris rivers.
  • D. Klaipėda
    Klaipėda is a Lithuanian port city on the Baltic Sea known as the country’s main maritime gateway and a key regional transport and industrial hub.
  • E. Vilnius
    Vilnius is the capital and largest city of Lithuania, known for its well-preserved medieval Old Town and rich cultural and historical heritage.
  • 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_69a8864b6de0819098d089f6a1b910a7 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb145f96c8190a71bb9e442892e68 completed March 7, 2026, 5:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69addf68a8d48190a3360557def67692 completed March 8, 2026, 8:43 p.m.
NEDg Description generation batch_69addff3e9a081908cba77103cd60171 completed March 8, 2026, 8:45 p.m.
NED2 Entity disambiguation (via description) batch_69ade07143808190b5dfe380428eb496 completed March 8, 2026, 8:47 p.m.
Created at: March 4, 2026, 7:34 p.m.