Triple

T737642
Position Surface form Disambiguated ID Type / Status
Subject King of Sweden E14968 entity
Predicate seat P75 FINISHED
Object Stockholm E14550 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: Stockholm | Statement: [King of Sweden, seat, Stockholm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Stockholm
Context triple: [King of Sweden, seat, Stockholm]
  • A. Stockholm chosen
    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.
  • B. Gothenburg
    Gothenburg is Sweden’s second-largest city, a major port on the country’s west coast known for its maritime heritage, universities, and vibrant cultural scene.
  • C. Uppsala
    Uppsala is a historic Swedish city north of Stockholm, known for its prestigious university, medieval cathedral, and role as a cultural and ecclesiastical center.
  • D. Old Town, Stockholm
    Old Town, Stockholm is the historic medieval center of Sweden’s capital, known for its cobblestone streets, colorful buildings, and major institutions like the Swedish Academy and the Royal Palace.
  • E. Östersund
    Östersund is a city in central Sweden known for its strong winter sports tradition and repeated bids to host the Winter Olympics.
  • 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_69a4934d9930819099eed80096b0597d completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a5effc108190aea8fbe86641ba54 completed March 1, 2026, 8:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69a6891b6d908190823a9a09cef5c7da completed March 3, 2026, 7:09 a.m.
Created at: March 1, 2026, 7:37 p.m.