Triple

T8952054
Position Surface form Disambiguated ID Type / Status
Subject Danny Huston E213374 entity
Predicate notableWork P4 FINISHED
Object Magic City
Magic City is a stylish period crime drama television series set in 1950s Miami, centered on the dark underworld surrounding a glamorous luxury hotel.
 E768661 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: [Danny Huston, notableWork, Magic City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Magic City
Context triple: [Danny Huston, notableWork, Magic City]
  • 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. Magic City
    Magic City is the nickname of Minot, North Dakota, reflecting its rapid early growth and development.
  • E. 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.
  • 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: [Danny Huston, notableWork, Magic City]
Generated description
Magic City is a stylish period crime drama television series set in 1950s Miami, centered on the dark underworld surrounding a glamorous luxury hotel.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Magic City
Target entity description: Magic City is a stylish period crime drama television series set in 1950s Miami, centered on the dark underworld surrounding a glamorous luxury hotel.
  • 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. Magic City
    Magic City is the nickname of Minot, North Dakota, reflecting its rapid early growth and development.
  • E. 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.
  • 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_69ca8399ad2081909f8fa41d4314c215 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc670dc0c88190b1f59e96ad88e4ee completed April 1, 2026, 12:30 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc20a5ab481909e10f3abf679ec4c completed April 3, 2026, 1:35 p.m.
NEDg Description generation batch_69cfc2d295c48190952486e6f44cd74f completed April 3, 2026, 1:38 p.m.
NED2 Entity disambiguation (via description) batch_69cfc722921881908978147e4cc6875c completed April 3, 2026, 1:56 p.m.
Created at: March 30, 2026, 6:59 p.m.