Triple

T1853403
Position Surface form Disambiguated ID Type / Status
Subject Columbia, South Carolina E41645 entity
Predicate nickname P55 FINISHED
Object Cola Town E206439 NE FINISHED

How this triple was built (2 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: Cola Town | Statement: [Columbia, South Carolina, nickname, Cola Town]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cola Town
Context triple: [Columbia, South Carolina, nickname, Cola Town]
  • A. Soda City chosen
    Soda City is a popular nickname for Columbia, South Carolina, reflecting the city's historic association with the soft drink industry and its vibrant local culture.
  • B. Chocolate City
    Chocolate City is a popular nickname for Washington, D.C., highlighting its historically large and influential African American population and culture.
  • C. Congo Town
    Congo Town is a small settlement on South Andros in The Bahamas, known as a local administrative and transportation hub with its own regional airport.
  • D. Camel City
    Camel City is a nickname for Winston-Salem, North Carolina, historically tied to the city’s association with the Camel cigarette brand and its tobacco industry.
  • E. Ochre City
    Ochre City is a popular nickname for Marrakesh, referring to the Moroccan city's distinctive red and ochre-colored buildings and walls.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69a8864a83848190a4ec02721306c511 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb06b3f08819092b3fbdff83b3097 completed March 7, 2026, 4:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69add1c89bdc8190acf517a7731fa5c7 completed March 8, 2026, 7:45 p.m.
Created at: March 4, 2026, 7:33 p.m.