Triple

T19339683
Position Surface form Disambiguated ID Type / Status
Subject Count of Nassau-Dietz E483718 entity
Predicate region P40 FINISHED
Object Friesland 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: Friesland | Statement: [Count of Nassau-Dietz, region, Friesland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Friesland
Context triple: [Count of Nassau-Dietz, region, Friesland]
  • A. Friesland chosen
    Friesland is a northern province of the Netherlands known for its distinct Frisian language, rich maritime history, and unique cultural traditions.
  • B. Zeeland
    Zeeland is a coastal province in the southwest of the Netherlands, known for its islands, peninsulas, and extensive dike and flood defense systems.
  • C. West-Friesland
    West-Friesland is a historical region in the northwest of the Netherlands known for its distinctive cultural identity, traditional landscapes, and old trading towns.
  • D. Drenthe, Netherlands
    Drenthe, Netherlands is a rural northeastern Dutch province known for its prehistoric dolmen tombs, extensive nature reserves, and quiet agricultural landscapes.
  • E. Kennemerland
    Kennemerland is a coastal historical region in the northwest of the Netherlands, known for its dunes, beaches, and old trading towns.
  • 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_69d8e8d244f8819080eb1f3491300db2 completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e618560f0081908565f802ea1e3cc8 completed April 20, 2026, 12:13 p.m.
Created at: April 10, 2026, 1:33 p.m.