Triple

T8965388
Position Surface form Disambiguated ID Type / Status
Subject Bloomington E214116 entity
Predicate borders P224 FINISHED
Object Eden Prairie E353221 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: Eden Prairie | Statement: [Bloomington, borders, Eden Prairie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eden Prairie
Context triple: [Bloomington, borders, Eden Prairie]
  • A. Eden Prairie chosen
    Eden Prairie is a suburban city in the Minneapolis–Saint Paul metropolitan area of Minnesota, known for its residential communities, parks, and corporate offices.
  • B. Robbinsdale
    Robbinsdale is a small suburban city in Hennepin County, Minnesota, located just northwest of Minneapolis.
  • C. Minnetonka
    Minnetonka is a suburban city in the Minneapolis–Saint Paul metropolitan area of Minnesota, known for its residential communities and proximity to Lake Minnetonka.
  • D. Burnsville, Minnesota
    Burnsville, Minnesota is a suburban city in the Minneapolis–Saint Paul metropolitan area known for its residential communities, commercial centers, and proximity to major transportation routes.
  • E. Rosemount, Minnesota
    Rosemount, Minnesota is a suburban city in the Twin Cities metropolitan area known for its mix of residential neighborhoods, industrial facilities, and proximity to both urban amenities and rural landscapes.
  • 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_69ca839cd6008190a1546a701a56710c completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc674c4be8819090d46aba8ab40af3 completed April 1, 2026, 12:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfd0a753f8819084b952f20997c8d6 completed April 3, 2026, 2:37 p.m.
Created at: March 30, 2026, 7:01 p.m.