Triple

T9625403
Position Surface form Disambiguated ID Type / Status
Subject Evangelical Church in the Rhineland E232449 entity
Predicate headquarters P62 FINISHED
Object Düsseldorf E36993 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: Düsseldorf | Statement: [Evangelical Church in the Rhineland, headquarters, Düsseldorf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Düsseldorf
Context triple: [Evangelical Church in the Rhineland, headquarters, Düsseldorf]
  • A. Düsseldorf chosen
    Düsseldorf is a major German city on the Rhine River known for its fashion and art scenes, modern architecture, and status as an important economic and financial center.
  • B. Cologne
    Cologne is a historic German city on the Rhine River, renowned for its Gothic cathedral, vibrant cultural scene, and status as a major economic and media hub.
  • C. 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.
  • D. Krefeld
    Krefeld is a city in western Germany near the Rhine River, known historically for its textile and silk industry.
  • E. Wuppertal
    Wuppertal is a city in western Germany known for its steep slopes, extensive parks, and the unique suspended monorail Wuppertal Schwebebahn.
  • 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_69ca848793ec8190a93a12383a754dc0 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9afb67c88190aa170716f0033752 completed April 1, 2026, 10:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69d8dbc697388190b384c7ed9e6a65dc completed April 10, 2026, 11:15 a.m.
Created at: March 30, 2026, 8:10 p.m.