Triple

T16410842
Position Surface form Disambiguated ID Type / Status
Subject Esslingen am Neckar E398557 entity
Predicate twinTown P1072 FINISHED
Object Norrköping, Sweden E289301 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: Norrköping, Sweden | Statement: [Esslingen am Neckar, twinTown, Norrköping, Sweden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Norrköping, Sweden
Context triple: [Esslingen am Neckar, twinTown, Norrköping, Sweden]
  • A. 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.
  • B. Norrköping chosen
    Norrköping is a historic industrial city in eastern Sweden known for its preserved textile mills, waterways, and cultural institutions.
  • 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. Nyköping
    Nyköping is a historic coastal town in southeastern Sweden known for its medieval castle, harbor, and role as a regional administrative and cultural center.
  • E. Landskrona, Sweden
    Landskrona, Sweden is a coastal city in southern Sweden’s Skåne County, known for its historic fortress, harbor, and local football culture.
  • 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_69d87f2950248190bc8ad9b9bebdc8c8 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e328731a408190b38dcab0b7bb65ff completed April 18, 2026, 6:45 a.m.
NED1 Entity disambiguation (via context triple) batch_6a003c66d41481909340f247f6e5393f completed May 10, 2026, 8:05 a.m.
Created at: April 10, 2026, 5:09 a.m.