Triple

T5196302
Position Surface form Disambiguated ID Type / Status
Subject Karen E. Spilka E117279 entity
Predicate familyName P18 FINISHED
Object Spilka
Spilka is a surname most notably associated with Karen E. Spilka, an American politician and attorney who has served as President of the Massachusetts Senate.
 E500844 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: Spilka | Statement: [Karen E. Spilka, familyName, Spilka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Spilka
Context triple: [Karen E. Spilka, familyName, Spilka]
  • A. Pikaliiva
    Pikaliiva is a residential subdistrict of Tallinn, Estonia, located within the Haabersti district.
  • B. Grocka
    Grocka is a suburban municipality of Belgrade in Serbia, known for its agricultural production, especially fruit growing, and its location along the Danube River.
  • C. Kukarka
    Kukarka is a small Russian locality historically known as the birthplace of Soviet politician Vyacheslav Molotov.
  • D. Kvasy
    Kvasy is a village in western Ukraine’s Zakarpattia region, known as a starting point for hikes in the Carpathian Mountains and for its mineral springs.
  • E. Pavka
    Pavka is a diminutive or affectionate nickname commonly used for the Slavic given name Pavel.
  • 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: Spilka
Triple: [Karen E. Spilka, familyName, Spilka]
Generated description
Spilka is a surname most notably associated with Karen E. Spilka, an American politician and attorney who has served as President of the Massachusetts Senate.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Spilka
Target entity description: Spilka is a surname most notably associated with Karen E. Spilka, an American politician and attorney who has served as President of the Massachusetts Senate.
  • A. Pikaliiva
    Pikaliiva is a residential subdistrict of Tallinn, Estonia, located within the Haabersti district.
  • B. Grocka
    Grocka is a suburban municipality of Belgrade in Serbia, known for its agricultural production, especially fruit growing, and its location along the Danube River.
  • C. Kukarka
    Kukarka is a small Russian locality historically known as the birthplace of Soviet politician Vyacheslav Molotov.
  • D. Kvasy
    Kvasy is a village in western Ukraine’s Zakarpattia region, known as a starting point for hikes in the Carpathian Mountains and for its mineral springs.
  • E. Pavka
    Pavka is a diminutive or affectionate nickname commonly used for the Slavic given name Pavel.
  • 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_69bd4462ed04819084fcb01eb9d2fa74 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd7a1c2184819083f4b1d8830bebae completed March 20, 2026, 4:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69bee09d84f08190b8270394fc35e195 completed March 21, 2026, 6:17 p.m.
NEDg Description generation batch_69bee5b5c6688190ab4dcdcf50424436 completed March 21, 2026, 6:38 p.m.
NED2 Entity disambiguation (via description) batch_69bee67364588190b5d8f31af7adf1f4 completed March 21, 2026, 6:41 p.m.
Created at: March 20, 2026, 1:46 p.m.