Triple

T23070528
Position Surface form Disambiguated ID Type / Status
Subject Gerrit Krol E575179 entity
Predicate residence P75 FINISHED
Object Groningen 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: Groningen | Statement: [Gerrit Krol, residence, Groningen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Groningen
Context triple: [Gerrit Krol, residence, Groningen]
  • A. Groningen chosen
    Groningen is a historic province in the northern Netherlands, known for its university city of the same name, flat landscapes, and rich maritime and agricultural heritage.
  • 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. Dordrecht
    Dordrecht is a historic Dutch city in South Holland known as one of the oldest trading centers in the Netherlands, situated strategically within the Rhine–Meuse–Scheldt river delta.
  • E. Leeuwarden
    Leeuwarden is a historic city in the northern Netherlands, known as the capital of the province of Friesland and for its rich cultural and architectural heritage.
  • 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_69e245bd6e4c8190bb8942245b68cad5 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f18c5f17348190ab92cfdae9bcaeba completed April 29, 2026, 4:43 a.m.
Created at: April 17, 2026, 3:56 p.m.