Triple

T16310654
Position Surface form Disambiguated ID Type / Status
Subject Rheinberg E396044 entity
Predicate locatedNear P294 FINISHED
Object Moers E495845 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: Moers | Statement: [Rheinberg, locatedNear, Moers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Moers
Context triple: [Rheinberg, locatedNear, Moers]
  • A. Moers chosen
    Moers is a city in western Germany’s North Rhine-Westphalia, known as a former coal-mining center on the western edge of the Ruhr industrial region.
  • B. Dieburg
    Dieburg is a small historic town in the German state of Hesse, known for its medieval old town and regional administrative role.
  • C. Roermond
    Roermond is a historic city in the southeastern Netherlands known for its medieval architecture, prominent churches, and large designer outlet shopping center.
  • D. Münster
    Münster is a historic city in western Germany known as one of the principal sites where the Peace of Westphalia treaties were negotiated and signed, ending the Thirty Years' War in 1648.
  • E. Xanten
    Xanten is a historic town in western Germany known for its well-preserved Roman archaeological park and medieval architecture.
  • 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_69d87f23bb088190a16fbb91a1957ea5 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e288da27f88190aa241e3addf9cd7f completed April 17, 2026, 7:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a001fa6ceb48190b937a15b94fd3cfa completed May 10, 2026, 6:03 a.m.
Created at: April 10, 2026, 5:06 a.m.