Triple

T4557941
Position Surface form Disambiguated ID Type / Status
Subject Godert de Ginkell E120523 entity
Predicate deathPlace P21 FINISHED
Object Utrecht E8157 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: Utrecht | Statement: [Godert de Ginkell, deathPlace, Utrecht]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Utrecht
Context triple: [Godert de Ginkell, deathPlace, Utrecht]
  • A. Utrecht chosen
    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.
  • B. 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.
  • C. Tilburg
    Tilburg is a city in the southern Netherlands known historically as an industrial and textile center and now as a regional cultural and educational hub.
  • D. Groningen
    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.
  • E. Leiden
    Leiden is a historic Dutch city in South Holland known for its prestigious university, rich cultural heritage, and well-preserved canals and old town.
  • 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_69bd4636f1648190a701445c2fcd9c17 completed March 20, 2026, 1:05 p.m.
NER Named-entity recognition batch_69bd58163e6081909d5a3aae12c42a00 completed March 20, 2026, 2:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69c95675f24c81909f9c14e29a79c157 completed March 29, 2026, 4:42 p.m.
Created at: March 20, 2026, 1:09 p.m.