Triple

T16576800
Position Surface form Disambiguated ID Type / Status
Subject Richel E402732 entity
Predicate countryAdminDivision P766 FINISHED
Object Friesland E15439 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: Friesland | Statement: [Richel, countryAdminDivision, Friesland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Friesland
Context triple: [Richel, countryAdminDivision, 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 (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_69d88387363c8190a97a0c942130de97 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3595dd90881909933216bd12505e1 completed April 18, 2026, 10:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a006edbd8388190b9a96c1cc5c9119e completed May 10, 2026, 11:41 a.m.
Created at: April 10, 2026, 5:16 a.m.