Triple

T2498449
Position Surface form Disambiguated ID Type / Status
Subject Yat Malmgren E52405 entity
Predicate birthPlace P1 FINISHED
Object Gävle, Sweden E179056 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: Gävle, Sweden | Statement: [Yat Malmgren, birthPlace, Gävle, Sweden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gävle, Sweden
Context triple: [Yat Malmgren, birthPlace, Gävle, Sweden]
  • A. Gävle chosen
    Gävle is a coastal city in eastern Sweden known as an important regional port, industrial center, and the home of the famous Gävle Christmas Goat.
  • B. Växjö, Sweden
    Växjö is a mid-sized city in southern Sweden known for its environmental sustainability initiatives, universities, and role as a regional cultural and economic center in Småland.
  • C. Karlskoga, Sweden
    Karlskoga, Sweden is an industrial town in central Sweden best known for its historic arms manufacturer Bofors and its association with Alfred Nobel.
  • D. Gävle Municipality
    Gävle Municipality is a local government area in east-central Sweden that includes the coastal city of Gävle, known for its industry, port, and regional administrative significance.
  • E. Södertälje, Sweden
    Södertälje, Sweden is an industrial city southwest of Stockholm known for its major manufacturing plants, particularly in the automotive and heavy vehicle sectors.
  • 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_69ab4957b3a88190adf968ae0c1b931c completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abd1ae9040819091b3ca5b98659e99 completed March 7, 2026, 7:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69af1f9ed13c81909856db636bfb2e9e completed March 9, 2026, 7:29 p.m.
Created at: March 6, 2026, 9:46 p.m.