Triple

T16293054
Position Surface form Disambiguated ID Type / Status
Subject Ennepe-Ruhr-Kreis E395573 entity
Predicate contains P35 FINISHED
Object Hattingen E195827 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: Hattingen | Statement: [Ennepe-Ruhr-Kreis, contains, Hattingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hattingen
Context triple: [Ennepe-Ruhr-Kreis, contains, Hattingen]
  • A. Hattingen chosen
    Hattingen is a historic town in North Rhine-Westphalia, Germany, known for its well-preserved medieval old town and its location in the Ruhr industrial region.
  • B. Rüttenscheid
    Rüttenscheid is a lively, upscale district of Essen, Germany, known for its bustling shopping streets, restaurants, and cultural venues.
  • C. Bergkamen
    Bergkamen is a town in North Rhine-Westphalia, Germany, known for its coal mining heritage and post-war planned urban development.
  • D. Backworth
    Backworth is a village in North Tyneside, England, historically associated with coal mining and now largely residential.
  • E. Rüdinghausen
    Rüdinghausen is a district of the city of Witten in North Rhine-Westphalia, Germany, characterized by its residential areas and local amenities.
  • 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_69d87f22c7248190a54c949738441e2e completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e25e2aee6881909fd28547f135427c completed April 17, 2026, 4:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a004f3d8f188190969b75d82c6b13f0 completed May 10, 2026, 9:26 a.m.
Created at: April 10, 2026, 5:05 a.m.