Triple

T11921212
Position Surface form Disambiguated ID Type / Status
Subject Marl, Germany E283658 entity
Predicate locatedNear P294 FINISHED
Object Herten E454557 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: Herten | Statement: [Marl, Germany, locatedNear, Herten]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Herten
Context triple: [Marl, Germany, locatedNear, Herten]
  • A. Herten chosen
    Herten is a town in the Ruhr area of North Rhine-Westphalia, western Germany, historically shaped by coal mining and now known for its transition to renewable energy and green urban development.
  • B. Hellebæk
    Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
  • C. Glostrup
    Glostrup is a suburban town and municipality in the Copenhagen metropolitan area of Denmark, known for its residential neighborhoods and commercial districts.
  • D. Hillerød
    Hillerød is a Danish town on the island of Zealand, known for the historic Frederiksborg Castle and its role as a regional administrative and cultural center.
  • E. Næstved
    Næstved is a historic market town and commercial center in southern Denmark, located on the island of Zealand.
  • 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_69d6ab2c07e88190ba13b0d21fd6cf33 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8e8e1b08481909ed291667035f330 completed April 10, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69f44033c288819099587af3f895eac5 completed May 1, 2026, 5:54 a.m.
Created at: April 8, 2026, 9:45 p.m.