Triple

T10095897
Position Surface form Disambiguated ID Type / Status
Subject Technical University of Denmark E215863 entity
Predicate city P40 FINISHED
Object Kongens Lyngby E569301 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: Kongens Lyngby | Statement: [Technical University of Denmark, city, Kongens Lyngby]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kongens Lyngby
Context triple: [Technical University of Denmark, city, Kongens Lyngby]
  • A. Ballerup
    Ballerup is a suburban municipality near Copenhagen in eastern Denmark, known for its residential areas, business parks, and sports facilities.
  • B. Hvidovre
    Hvidovre is a suburban municipality in the Capital Region of Denmark, located just southwest of central Copenhagen.
  • C. Lyngby-Taarbæk Municipality chosen
    Lyngby-Taarbæk Municipality is a suburban municipality north of Copenhagen known for its educational institutions, green areas, and role as part of the Greater Copenhagen area.
  • D. Rødovre
    Rødovre is a suburban municipality in the Capital Region of Denmark, located just west of central Copenhagen.
  • E. Herlev
    Herlev is a suburban municipality and town in the Capital Region of Denmark, located just northwest of central Copenhagen.
  • 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_69ca83a4947c8190823a7495dc5d96ed completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdd0798c248190af675e30e280daa8 completed April 2, 2026, 2:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2e591be44819094623fd11f6fccdb completed April 5, 2026, 10:43 p.m.
Created at: March 30, 2026, 9:02 p.m.