Triple

T2963049
Position Surface form Disambiguated ID Type / Status
Subject Erlangen E80092 entity
Predicate twinTown P1072 FINISHED
Object Eskilstuna E203558 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: Eskilstuna | Statement: [Erlangen, twinTown, Eskilstuna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eskilstuna
Context triple: [Erlangen, twinTown, Eskilstuna]
  • A. Eskilstuna chosen
    Eskilstuna is an industrial city in central Sweden known historically for its metalworking and engineering industries.
  • B. Enköping
    Enköping is a small Swedish town known for its numerous themed parks and gardens, often called “Sweden’s nearest town” due to its central location relative to several major cities.
  • C. 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.
  • D. Sundsvall
    Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
  • E. Norrköping
    Norrköping is a historic industrial city in eastern Sweden known for its preserved textile mills, waterways, and cultural institutions.
  • 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_69ad8b1341848190bd19dbf46892887d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9957602c819089b673966fd619e0 completed March 8, 2026, 3:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69b276cc19b48190a952015c9e501dd6 completed March 12, 2026, 8:18 a.m.
Created at: March 8, 2026, 2:57 p.m.