Triple

T1622577
Position Surface form Disambiguated ID Type / Status
Subject Montevideo City Torque E35064 entity
Predicate shortName P43 FINISHED
Object City Torque
City Torque is a Uruguayan professional football club based in Montevideo that competes in the country’s top divisions.
 E185098 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: City Torque | Statement: [Montevideo City Torque, shortName, City Torque]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: City Torque
Context triple: [Montevideo City Torque, shortName, City Torque]
  • A. Chocolate City
    Chocolate City is a popular nickname for Washington, D.C., highlighting its historically large and influential African American population and culture.
  • B. Maximum City
    Maximum City is a popular nickname for Mumbai that reflects its vast scale, intense energy, and extreme contrasts in wealth, culture, and daily life.
  • C. City Loop
    City Loop is Melbourne’s central underground railway system that circulates suburban trains through key inner-city stations.
  • D. المدينة
    المدينة هي الاسم العربي المختصر لمدينة المدينة المنورة، إحدى أقدس المدن في الإسلام وثاني أقدس موقع بعد مكة المكرمة.
  • E. The City
    The City is a common nickname for Manhattan, the densely populated and iconic borough of New York City known for its skyscrapers, cultural landmarks, and role as a global financial and media hub.
  • 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: City Torque
Triple: [Montevideo City Torque, shortName, City Torque]
Generated description
City Torque is a Uruguayan professional football club based in Montevideo that competes in the country’s top divisions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: City Torque
Target entity description: City Torque is a Uruguayan professional football club based in Montevideo that competes in the country’s top divisions.
  • A. Chocolate City
    Chocolate City is a popular nickname for Washington, D.C., highlighting its historically large and influential African American population and culture.
  • B. Maximum City
    Maximum City is a popular nickname for Mumbai that reflects its vast scale, intense energy, and extreme contrasts in wealth, culture, and daily life.
  • C. City Loop
    City Loop is Melbourne’s central underground railway system that circulates suburban trains through key inner-city stations.
  • D. المدينة
    المدينة هي الاسم العربي المختصر لمدينة المدينة المنورة، إحدى أقدس المدن في الإسلام وثاني أقدس موقع بعد مكة المكرمة.
  • E. The City
    The City is a common nickname for Manhattan, the densely populated and iconic borough of New York City known for its skyscrapers, cultural landmarks, and role as a global financial and media hub.
  • 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_69a886023194819080a3fccd6e325d0e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a909cf3c7481909ddbe6a6596bb0c8 completed March 5, 2026, 4:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad58ccc80c819088ecd91f0a99a247 completed March 8, 2026, 11:09 a.m.
NEDg Description generation batch_69ad5a619da481908d66837ea94c91cf completed March 8, 2026, 11:15 a.m.
NED2 Entity disambiguation (via description) batch_69ad5b41a68c8190ba293d8e8c35521b completed March 8, 2026, 11:19 a.m.
Created at: March 4, 2026, 7:28 p.m.