Triple

T18764522
Position Surface form Disambiguated ID Type / Status
Subject Jylland E458858 entity
Predicate containsCity P294 FINISHED
Object Silkeborg 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: Silkeborg | Statement: [Jylland, containsCity, Silkeborg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Silkeborg
Context triple: [Jylland, containsCity, Silkeborg]
  • A. Silkeborg chosen
    Silkeborg is a Danish town in central Jutland known for its surrounding lake district, forests, and outdoor recreation.
  • B. Holstebro
    Holstebro is a town in western Jutland, Denmark, known as a regional center that hosts significant Danish Army military facilities.
  • C. Sønderborg
    Sønderborg is a coastal town in southern Denmark known for its historic castle, waterfront setting on the island of Als, and role as a regional cultural and educational center.
  • D. Holbæk
    Holbæk is a coastal town and municipality in northwestern Zealand, Denmark, known for its harbor on Holbæk Fjord and role as a regional commercial and cultural center.
  • E. Skjern
    Skjern is a town in western Jutland, Denmark, known for its location near the Skjern River and its surrounding agricultural landscape.
  • 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_69d8d395dba0819087568404508590cb completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e58d82297c81909f720e2637cec737 completed April 20, 2026, 2:20 a.m.
Created at: April 10, 2026, 11:52 a.m.