Triple

T15968923
Position Surface form Disambiguated ID Type / Status
Subject Löhne E387268 entity
Predicate hasTwinTown P919 FINISHED
Object Koło E59958 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: Koło | Statement: [Löhne, hasTwinTown, Koło]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Koło
Context triple: [Löhne, hasTwinTown, Koło]
  • A. Koło chosen
    Koło is a town in central Poland that served as a transit point for Jews and other victims deported to the Chełmno extermination camp during the Holocaust.
  • B. Kolo
    Kolo is the given name of Kolo Touré, a retired Ivorian professional footballer known for his successful career as a defender in the English Premier League and with the Ivory Coast national team.
  • C. Kolo
    Kolo is a town in Ogbia Local Government Area of Bayelsa State in Nigeria, known as one of the communities in the Niger Delta region.
  • D. Kolo
    Kolo is an alternative name for the Ewondo language, a Bantu language spoken primarily in Cameroon.
  • E. Cerchio
    Cerchio is a small Italian town in the Abruzzo region, known for its location within the mountainous Sirente-Velino area.
  • 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_69d86da94ccc819083d187f5dc6a123e completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1572847f08190830e30125e829766 completed April 16, 2026, 9:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffbe88fa308190942d37cf67458396 completed May 9, 2026, 11:08 p.m.
Created at: April 10, 2026, 4:54 a.m.