Triple

T96443
Position Surface form Disambiguated ID Type / Status
Subject Finnish Army E1941 entity
Predicate headquartersLocation P62 FINISHED
Object Helsinki E14163 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: Helsinki | Statement: [Finnish Army, headquartersLocation, Helsinki]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Helsinki
Context triple: [Finnish Army, headquartersLocation, Helsinki]
  • A. Helsinki chosen
    Helsinki is the capital and largest city of Finland, known for its coastal location on the Baltic Sea, modern design, and vibrant cultural life.
  • B. Stockholm
    Stockholm is the capital city of Sweden, renowned for its historic architecture, cultural institutions, and role as a major political, economic, and scientific center in Scandinavia.
  • C. Copenhagen
    Copenhagen is the capital and largest city of Denmark, known for its historic architecture, vibrant cultural scene, and high quality of life.
  • D. Oslo
    Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
  • E. Hamburg
    Hamburg is Germany’s second-largest city and a major northern European port and cultural center on the River Elbe.
  • 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_69a24d4862f881908cc8b89d3a78031d completed Feb. 28, 2026, 2:04 a.m.
NER Named-entity recognition batch_69a24fd5cecc8190aca5fb4c4fe91a19 completed Feb. 28, 2026, 2:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2bf6620c88190bfc1c65b9942c98f completed Feb. 28, 2026, 10:11 a.m.
Created at: Feb. 28, 2026, 2:09 a.m.