Triple

T9749687
Position Surface form Disambiguated ID Type / Status
Subject Rhine-Ruhr S-Bahn E236408 entity
Predicate servesCity P82 FINISHED
Object Neuss E147996 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: Neuss | Statement: [Rhine-Ruhr S-Bahn, servesCity, Neuss]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Neuss
Context triple: [Rhine-Ruhr S-Bahn, servesCity, Neuss]
  • A. Neuss chosen
    Neuss is a city in western Germany, near Düsseldorf, known as an administrative and commercial center with historical roots dating back to Roman times.
  • B. Neunkirchen
    Neunkirchen is a town in southwestern Germany known as one of the major urban centers and former industrial hubs of the state of Saarland.
  • C. Neunkirchen
    Neunkirchen is an industrial town in Austria’s Lower Austria region, known historically for its manufacturing and metalworking industries.
  • D. Bitburg
    Bitburg is a town in western Germany’s Eifel region, best known internationally for its Bitburger brewery and its nearby World War II military cemetery.
  • E. Eschweiler
    Eschweiler is a town in western Germany near Aachen, known for its industrial history and location in the state of North Rhine-Westphalia.
  • 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_69ca84d4eddc8190996fec1417d2bae8 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9f6a2f8c8190a6f6af6587ee90b8 completed April 1, 2026, 10:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d2699ec50481908d83d3d22e102fb2 completed April 5, 2026, 1:54 p.m.
Created at: March 30, 2026, 8:24 p.m.