Triple

T15197463
Position Surface form Disambiguated ID Type / Status
Subject Älvsborg County E363173 entity
Predicate containsCity P294 FINISHED
Object Trollhättan E18308 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: Trollhättan | Statement: [Älvsborg County, containsCity, Trollhättan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Trollhättan
Context triple: [Älvsborg County, containsCity, 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 (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_69d85a0b78bc8190b6e5ad51a2c4cfc5 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0067fcc788190abdc083d4eadeb36 completed April 15, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00a502c82881908d5b6f7c23e8a403 completed May 10, 2026, 3:32 p.m.
Created at: April 10, 2026, 3:10 a.m.