Triple

T10064631
Position Surface form Disambiguated ID Type / Status
Subject Braunfels Castle E213068 entity
Predicate near P350 FINISHED
Object Lahn River region E751125 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: Lahn River region | Statement: [Braunfels Castle, near, Lahn River region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lahn River region
Context triple: [Braunfels Castle, near, Lahn River region]
  • A. Lahn region chosen
    The Lahn region is a scenic area in western Germany centered around the Lahn River, known for its historic towns, castles, and wine-growing landscapes.
  • B. Saale-Holzland region
    The Saale-Holzland region is a rural district in the German state of Thuringia, known for its Saale River landscapes, forests, and small historic towns.
  • C. Unstrut River region
    The Unstrut River region is a historical landscape in central Germany characterized by the Unstrut River and its surrounding valleys, towns, and agricultural areas.
  • D. Hase River region
    The Hase River region is an area in northwestern Germany characterized by the course of the Hase River and the surrounding rural landscapes and towns.
  • E. Saale River valley
    The Saale River valley is a scenic river landscape in central Germany characterized by winding waterways, steep slopes, and a mix of forests, vineyards, and historic towns.
  • 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_69ca83977128819084084eb7d1d8c52a completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdcfd653748190aeddf7a679028604 completed April 2, 2026, 2:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b630ca008190a337660ad8c9d57e completed April 5, 2026, 7:21 p.m.
Created at: March 30, 2026, 8:58 p.m.