Triple

T21840751
Position Surface form Disambiguated ID Type / Status
Subject European meteorological services E539244 entity
Predicate hasMember P10 FINISHED
Object KNMI 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: KNMI | Statement: [European meteorological services, hasMember, KNMI]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: KNMI
Context triple: [European meteorological services, hasMember, KNMI]
  • A. KNMI chosen
    KNMI is the Royal Netherlands Meteorological Institute, responsible for national weather forecasting, climate research, and seismic monitoring in the Netherlands.
  • B. KNMI weather station Gilze-Rijen
    KNMI weather station Gilze-Rijen is a Dutch meteorological station operated by the Royal Netherlands Meteorological Institute, providing official weather and climate observations for the Gilze en Rijen area.
  • C. Wetteren
    Wetteren is a municipality in the East Flanders province of Belgium, situated along the River Scheldt and known for its horticulture and residential character.
  • D. Woudwetering
    Woudwetering is a Dutch waterway in South Holland that serves as an important local canal near the village of Woubrugge.
  • 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_69e0c476c3c88190a92d08ebb59a128a completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69f0a7ab71e081908e3d3293743e6409 completed April 28, 2026, 12:27 p.m.
Created at: April 16, 2026, 6:55 p.m.