Triple

T5467463
Position Surface form Disambiguated ID Type / Status
Subject Luther College E122746 entity
Predicate city P40 FINISHED
Object Decorah E338597 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: Decorah | Statement: [Luther College, city, Decorah]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Decorah
Context triple: [Luther College, city, Decorah]
  • A. Decorah chosen
    Decorah is a small city in northeastern Iowa known for its scenic bluffs, Norwegian-American heritage, and as the home of Luther College.
  • B. Orono
    Orono is a suburban city in Minnesota known for its affluent residential communities and scenic location along the north shore of Lake Minnetonka.
  • C. Orono
    Orono is a small rural village in Ontario, Canada, known for its historic downtown, agricultural surroundings, and community events.
  • D. Houghton
    Houghton is a wealthy, historically significant suburb of Johannesburg, South Africa, known for being the longtime home of Nelson Mandela.
  • E. Houghton
    Houghton is a residential and waterfront neighborhood in the city of Kirkland, Washington, known for its parks, lake access, and small-town feel within the greater Seattle metropolitan area.
  • 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_69bd4643f16081908d7f29e08096115a completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd9218621c819093267a012bd49a35 completed March 20, 2026, 6:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf488da1d8819091e1cad0500b1747 completed March 22, 2026, 1:40 a.m.
Created at: March 20, 2026, 2:08 p.m.