Triple

T18796837
Position Surface form Disambiguated ID Type / Status
Subject Trollhättan main campus E459656 entity
Predicate city P40 FINISHED
Object Trollhättan NE NERFINISHED

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: Trollhättan | Statement: [Trollhättan main campus, city, Trollhättan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Trollhättan
Context triple: [Trollhättan main campus, city, Trollhättan]
  • A. Trollhättan chosen
    Trollhättan is a city in western Sweden known for its historic role in the automotive industry and as the longtime home of Saab Automobile’s main production facilities.
  • B. Skellefteå
    Skellefteå is a city in northern Sweden known for its growing high-tech and green industry sector, particularly in battery manufacturing, as well as its ice hockey tradition.
  • C. Södertälje
    Södertälje is a Swedish city southwest of Stockholm known for its industrial heritage, diverse population, and strategic location linking Lake Mälaren with the Baltic Sea via the Södertälje Canal.
  • D. Sundsvall
    Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
  • E. Karlskoga
    Karlskoga is an industrial town in central Sweden known for its historical association with Alfred Nobel and its role in the country’s arms and engineering industries.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8d398c7d4819091cb2f7e48948aeb completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5a01ed3c08190890d9518ed69fdee completed April 20, 2026, 3:40 a.m.
Created at: April 10, 2026, 11:53 a.m.