Triple

T3877737
Position Surface form Disambiguated ID Type / Status
Subject Nederrijn E92543 entity
Predicate flowsThrough P225 FINISHED
Object Rhenen E323464 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: Rhenen | Statement: [Nederrijn, flowsThrough, Rhenen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rhenen
Context triple: [Nederrijn, flowsThrough, Rhenen]
  • A. Rhenen chosen
    Rhenen is a historic town and municipality in the central Netherlands, known for its scenic location along the Rhine and landmarks such as the Cunera Church and Ouwehands Zoo.
  • B. Rheinberg
    Rheinberg is a historic town in western Germany, situated on the Lower Rhine and known for its medieval architecture and role in various European military conflicts.
  • C. Wassenaar
    Wassenaar is an affluent coastal town in the western Netherlands known for its wooded estates, beaches, and role as a residential area for diplomats and expatriates.
  • D. Woudenberg
    Woudenberg is a small Dutch municipality and town located in the central Netherlands.
  • E. Brackenberg
    Brackenberg is an early recorded historical name for the Brocken, the highest peak in Germany’s Harz Mountains.
  • 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_69aed967448c819086c4b358d37b25aa completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeec72fa7c81909c73b3cf90597e9a completed March 9, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69b51c87214881908e03f5c770c58713 completed March 14, 2026, 8:29 a.m.
Created at: March 9, 2026, 3:20 p.m.