Triple

T14055819
Position Surface form Disambiguated ID Type / Status
Subject Copenhagen Central Station E338216 entity
Predicate connectsTo P845 FINISHED
Object Aarhus E101867 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: Aarhus | Statement: [Copenhagen Central Station, connectsTo, Aarhus]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aarhus
Context triple: [Copenhagen Central Station, connectsTo, Aarhus]
  • A. Aarhus chosen
    Aarhus is Denmark’s second-largest city, a major cultural and economic center on the Jutland peninsula known for its universities, vibrant arts scene, and historic harbor.
  • B. Aalborg
    Aalborg is a major city in northern Denmark known for its historic architecture, vibrant cultural life, and role as a regional economic and educational center.
  • C. Odense
    Odense is a historic Danish city on the island of Funen, best known as the birthplace of fairy-tale author Hans Christian Andersen and a cultural hub with museums, festivals, and a vibrant literary heritage.
  • D. Randers
    Randers is a historic market town and one of the largest cities in eastern Jutland, Denmark, known for its old town center and location along the Gudenå River.
  • E. Herning
    Herning is a Danish city in the Central Jutland region known for its trade fairs, conference facilities, and vibrant cultural and sports events.
  • 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_69d81c67ba6c819091935650dfb3b895 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de3c8d1aa48190a5055d15d9fa220c completed April 14, 2026, 1:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd27fdad4c8190bf5ce5d676284e62 completed May 8, 2026, 12:02 a.m.
Created at: April 9, 2026, 10:20 p.m.