Triple

T839389
Position Surface form Disambiguated ID Type / Status
Subject Michiel de Ruyter E18142 entity
Predicate placeOfBirth P1 FINISHED
Object County of Zeeland E13728 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: County of Zeeland | Statement: [Michiel de Ruyter, placeOfBirth, County of Zeeland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: County of Zeeland
Context triple: [Michiel de Ruyter, placeOfBirth, County of Zeeland]
  • A. County of Holland
    The County of Holland was a historically significant medieval and early modern county in the Low Countries that formed the political and economic core of what later became the Netherlands.
  • B. Zeeland chosen
    Zeeland is a coastal province in the southwest of the Netherlands, known for its islands, peninsulas, and extensive dike and flood defense systems.
  • C. Flevoland
    Flevoland is the youngest Dutch province, largely created through land reclamation from the IJsselmeer in the central Netherlands.
  • D. Schouwen-Duiveland
    Schouwen-Duiveland is a coastal municipality and island in the southwest of the Netherlands known for its beaches, nature reserves, and water sports tourism.
  • E. Overijssel
    Overijssel is a province in the eastern Netherlands known for its historic Hanseatic cities, rivers, and varied landscapes of forests, heathlands, and farmland.
  • 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_69a49389f44881909a608fb27d89f247 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4abe4ab1081909207ae2eec1898d9 completed March 1, 2026, 9:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69adbf33c7808190962fb6c2496b4ec7 completed March 8, 2026, 6:25 p.m.
Created at: March 1, 2026, 7:38 p.m.