Triple

T437050
Position Surface form Disambiguated ID Type / Status
Subject Milwaukee E10031 entity
Predicate nicknamed P744 FINISHED
Object Cream City
Cream City is a nickname for Milwaukee, Wisconsin, derived from the distinctive light-colored cream brick used in many of its historic buildings.
 E59878 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: Cream City | Statement: [Milwaukee, nicknamed, Cream City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Cream City
Context triple: [Milwaukee, nicknamed, Cream City]
  • A. Chi-Town
    Chi-Town is a popular nickname for the city of Chicago, reflecting its identity as a major cultural and economic hub in the United States.
  • B. Rain City
    Rain City is a popular nickname for Vancouver, a coastal Canadian city known for its frequent rainfall and lush, temperate climate.
  • C. Bellevue
    Bellevue is a small municipality located along Lake Geneva in the canton of Geneva in southwestern Switzerland.
  • D. River City
    River City is a popular nickname for Sacramento, California, highlighting the city’s close connection to the nearby American and Sacramento Rivers.
  • E. River City
    River City is a popular nickname for Wuhan, a major central Chinese metropolis known for its location at the confluence of the Yangtze and Han rivers.
  • 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: Cream City
Triple: [Milwaukee, nicknamed, Cream City]
Generated description
Cream City is a nickname for Milwaukee, Wisconsin, derived from the distinctive light-colored cream brick used in many of its historic buildings.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Cream City
Target entity description: Cream City is a nickname for Milwaukee, Wisconsin, derived from the distinctive light-colored cream brick used in many of its historic buildings.
  • A. Chi-Town
    Chi-Town is a popular nickname for the city of Chicago, reflecting its identity as a major cultural and economic hub in the United States.
  • B. Rain City
    Rain City is a popular nickname for Vancouver, a coastal Canadian city known for its frequent rainfall and lush, temperate climate.
  • C. Bellevue
    Bellevue is a small municipality located along Lake Geneva in the canton of Geneva in southwestern Switzerland.
  • D. River City
    River City is a popular nickname for Wuhan, a major central Chinese metropolis known for its location at the confluence of the Yangtze and Han rivers.
  • E. River City
    River City is a popular nickname for Sacramento, California, highlighting the city’s close connection to the nearby American and Sacramento Rivers.
  • 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_69a2e8465ef481909655c681b01e2986 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2ef0c97188190b62104cb639d4b60 completed Feb. 28, 2026, 1:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69a46c58de0881908c09850b6ceac8c6 completed March 1, 2026, 4:42 p.m.
NEDg Description generation batch_69a46cc070f48190a58b65b67efa25de completed March 1, 2026, 4:43 p.m.
NED2 Entity disambiguation (via description) batch_69a46d2002d8819086691b73f8fbaae2 completed March 1, 2026, 4:45 p.m.
Created at: Feb. 28, 2026, 1:11 p.m.