Triple

T15676911
Position Surface form Disambiguated ID Type / Status
Subject Ruhr uprising of 1920 E377465 entity
Predicate tookPlaceIn P40 FINISHED
Object Mülheim an der Ruhr E348208 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: Mülheim an der Ruhr | Statement: [Ruhr uprising of 1920, tookPlaceIn, Mülheim an der Ruhr]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mülheim an der Ruhr
Context triple: [Ruhr uprising of 1920, tookPlaceIn, Mülheim an der Ruhr]
  • A. Mülheim an der Ruhr chosen
    Mülheim an der Ruhr is a city in western Germany’s Ruhr area, known for its industrial heritage, riverside setting on the Ruhr River, and role as a regional economic and cultural center.
  • B. Duisburg
    Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
  • C. Recklinghausen
    Recklinghausen is a city in the Ruhr area of North Rhine-Westphalia, western Germany, known historically for coal mining and its role as a regional administrative center.
  • D. Gelsenkirchen
    Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
  • E. Krefeld
    Krefeld is a city in western Germany near the Rhine River, known historically for its textile and silk industry.
  • 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_69d85cd2e28481909d4e975bee20872f completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04f2e10a4819097eba1ea31e36ac2 completed April 16, 2026, 2:53 a.m.
NED1 Entity disambiguation (via context triple) batch_6a009d232074819083f58de3ee5fbf7d completed May 10, 2026, 2:58 p.m.
Created at: April 10, 2026, 4:16 a.m.