Triple

T8518395
Position Surface form Disambiguated ID Type / Status
Subject Minot, North Dakota E201633 entity
Predicate nickname P55 FINISHED
Object Magic City
Magic City is the nickname of Minot, North Dakota, reflecting its rapid early growth and development.
 E739573 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: Magic City | Statement: [Minot, North Dakota, nickname, Magic City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Magic City
Context triple: [Minot, North Dakota, nickname, Magic City]
  • A. Magic City
    Magic City is a popular nickname for Miami, highlighting the city's rapid growth, vibrant nightlife, and dynamic cultural scene.
  • B. Magic City
    Magic City is a nickname for Roanoke, Virginia, reflecting its rapid growth and development during the late 19th and early 20th centuries.
  • C. Magic City
    Magic City is the nickname of Billings, Montana, reflecting its rapid growth from a small railroad town into the state’s largest city.
  • D. The Magic City
    The Magic City is a nickname for Birmingham, Alabama, highlighting its rapid growth during the late 19th and early 20th centuries as an industrial and economic center.
  • E. Winter City
    Winter City is the popular nickname for Östersund, a Swedish town renowned for its cold climate and strong winter sports culture.
  • 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: Magic City
Triple: [Minot, North Dakota, nickname, Magic City]
Generated description
Magic City is the nickname of Minot, North Dakota, reflecting its rapid early growth and development.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Magic City
Target entity description: Magic City is the nickname of Minot, North Dakota, reflecting its rapid early growth and development.
  • A. Magic City
    Magic City is the nickname of Billings, Montana, reflecting its rapid growth from a small railroad town into the state’s largest city.
  • B. Magic City
    Magic City is a popular nickname for Miami, highlighting the city's rapid growth, vibrant nightlife, and dynamic cultural scene.
  • C. Magic City
    Magic City is a nickname for Roanoke, Virginia, reflecting its rapid growth and development during the late 19th and early 20th centuries.
  • D. The Magic City
    The Magic City is a nickname for Birmingham, Alabama, highlighting its rapid growth during the late 19th and early 20th centuries as an industrial and economic center.
  • E. Winter City
    Winter City is the popular nickname for Östersund, a Swedish town renowned for its cold climate and strong winter sports culture.
  • 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_69ca8321bb44819081b74df0b710276d completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe626787c819087e72dd76b2d9310 completed March 31, 2026, 3:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce4e6c93d081909da2a748b0fa6fd3 completed April 2, 2026, 11:09 a.m.
NEDg Description generation batch_69ce4ffc30e08190b71e941d63d56015 completed April 2, 2026, 11:16 a.m.
NED2 Entity disambiguation (via description) batch_69ce54dc664081908ff63ec7f92834d7 completed April 2, 2026, 11:37 a.m.
Created at: March 30, 2026, 6:16 p.m.