Triple

T3737975
Position Surface form Disambiguated ID Type / Status
Subject ZB Commodore E79630 entity
Predicate assembly P19323 FINISHED
Object Rüsselsheim, Germany E170536 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: Rüsselsheim, Germany | Statement: [ZB Commodore, assembly, Rüsselsheim, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rüsselsheim, Germany
Context triple: [ZB Commodore, assembly, Rüsselsheim, Germany]
  • A. Rüsselsheim am Main chosen
    Rüsselsheim am Main is a city in the German state of Hesse best known as a major automotive hub and the longtime home of car manufacturer Opel.
  • B. Weinheim, Germany
    Weinheim, Germany is a town in the state of Baden-Württemberg known for its historic old town, twin castles, and role as a regional economic and publishing center.
  • C. Schröttinghausen, Germany
    Schröttinghausen is a small locality in Germany best known as the birthplace of influential astronomer Walter Baade.
  • D. Rheinbach, Germany
    Rheinbach is a small town in the Rhein-Sieg district of North Rhine-Westphalia, western Germany, known for its glassmaking tradition and proximity to Bonn.
  • E. Giessen, Germany
    Giessen, Germany is a central German university town in the state of Hesse, known for its large student population and academic institutions.
  • 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_69ad8b115610819095b02007da5ca3cb completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcb3e9248819098d481fe29e1c628 completed March 8, 2026, 7:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4db20bdfc81909cd27278ff5d9716 completed March 14, 2026, 3:50 a.m.
Created at: March 8, 2026, 3:34 p.m.