Triple

T1395621
Position Surface form Disambiguated ID Type / Status
Subject Battle of the Ruhr E30657 entity
Predicate target P860 FINISHED
Object Hagen E291575 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: Hagen | Statement: [Battle of the Ruhr, target, Hagen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hagen
Context triple: [Battle of the Ruhr, target, Hagen]
  • A. Hagen chosen
    Hagen is a city in the Ruhr region of North Rhine-Westphalia in western Germany, known historically as an industrial and transport hub.
  • B. Kleve
    Kleve is a historic town in western Germany near the Dutch border, known for its medieval castle and role as the former capital of the Duchy of Cleves.
  • 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. Wuppertal
    Wuppertal is a city in western Germany known for its steep slopes, extensive parks, and the unique suspended monorail Wuppertal Schwebebahn.
  • E. Siegen
    Siegen is a city in western Germany known as the birthplace of the Baroque painter Peter Paul Rubens and for its historic mining and university traditions.
  • 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_69a498fd4e408190bd73eca30ea9754c completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c37fd6e0819084d610ef041db3af completed March 1, 2026, 10:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4dadd801481909551f6ed73e02fc2 completed March 14, 2026, 3:49 a.m.
Created at: March 1, 2026, 7:59 p.m.