Triple

T8980830
Position Surface form Disambiguated ID Type / Status
Subject Roermond E214518 entity
Predicate hasTwinTown P919 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: [Roermond, hasTwinTown, Neuss]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Neuss
Context triple: [Roermond, hasTwinTown, 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. 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.
  • D. Eschweiler
    Eschweiler is a town in western Germany near Aachen, known for its industrial history and location in the state of North Rhine-Westphalia.
  • E. Heinsberg
    Heinsberg is a town in western Germany’s North Rhine-Westphalia near the Dutch border, known as the administrative center of the Heinsberg district.
  • 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_69ca839ea8b88190922c6a326ffcc0d3 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc67a76f748190a4abad5d53d58fa8 completed April 1, 2026, 12:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69d110090f208190bf0338a37bb28e8b completed April 4, 2026, 1:20 p.m.
Created at: March 30, 2026, 7:03 p.m.