Triple

T19422390
Position Surface form Disambiguated ID Type / Status
Subject River Seseke E485888 entity
Predicate flowsThrough P225 FINISHED
Object Unna district 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: Unna district | Statement: [River Seseke, flowsThrough, Unna district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Unna district
Context triple: [River Seseke, flowsThrough, Unna district]
  • A. Unna district chosen
    Unna district is a Landkreis in the German state of North Rhine-Westphalia, located in the eastern Ruhr area and known for its mix of industrial towns and suburban communities.
  • B. Bilasuvar District
    Bilasuvar District is an administrative region in southern Azerbaijan known for its agricultural activities and location near the Iranian border.
  • C. Tillo District
    Tillo District is an administrative district in southeastern Turkey known for its historical and religious significance within Siirt Province.
  • D. Lillafüred district
    Lillafüred district is a scenic neighborhood of Miskolc in northeastern Hungary, known for its lakeside setting, historic hotel, and surrounding Bükk Mountains.
  • E. Hallunda district
    Hallunda district is a suburban residential area in Botkyrka Municipality, south of central Stockholm, Sweden.
  • 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_69d8e8d688f881909c85104a62e09d8a completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e632159d7081909d004544ec5992c0 completed April 20, 2026, 2:03 p.m.
Created at: April 10, 2026, 1:37 p.m.