Triple

T16278982
Position Surface form Disambiguated ID Type / Status
Subject Renkum E395212 entity
Predicate hasBorderWith P224 FINISHED
Object Wageningen 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: Wageningen | Statement: [Renkum, hasBorderWith, Wageningen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wageningen
Context triple: [Renkum, hasBorderWith, Wageningen]
  • A. Wageningen chosen
    Wageningen is a Dutch town historically significant as the site where German forces in the Netherlands formally surrendered at the end of World War II.
  • B. Utrecht
    Utrecht is a historic city and province in the central Netherlands, known for its medieval old town, canals, and role as a religious and cultural center.
  • C. Utrecht
    Utrecht is a small town in South Africa’s KwaZulu-Natal province, known for its scenic surroundings and historical significance dating back to the 19th century.
  • D. Nijmegen
    Nijmegen is a historic Dutch city near the German border that played a crucial strategic role during World War II, particularly in the Allied advance in 1944.
  • E. Hoogeveen
    Hoogeveen is a town and municipality in the northeastern Netherlands known for its historical peat colonies and location in the province of Drenthe.
  • 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_69d87f22c7248190a54c949738441e2e completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e24610908c8190921e507dcb4d8250 completed April 17, 2026, 2:39 p.m.
Created at: April 10, 2026, 5:05 a.m.