Triple

T26139726
Position Surface form Disambiguated ID Type / Status
Subject Lutherstadt Wittenberg–Bad Schmiedeberg–Pretzsch railway E659482 entity
Predicate locatedIn P40 FINISHED
Object Elbe-Elster region
The Elbe-Elster region is a rural area in eastern Germany, named after the Elbe and Elster rivers, known for its forests, wetlands, and small towns in the states of Brandenburg and Saxony.
E1716829 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: Elbe-Elster region | Statement: [Lutherstadt Wittenberg–Bad Schmiedeberg–Pretzsch railway, locatedIn, Elbe-Elster region]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Elbe-Elster region
Triple: [Lutherstadt Wittenberg–Bad Schmiedeberg–Pretzsch railway, locatedIn, Elbe-Elster region]
Generated description
The Elbe-Elster region is a rural area in eastern Germany, named after the Elbe and Elster rivers, known for its forests, wetlands, and small towns in the states of Brandenburg and Saxony.

Provenance (5 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_69ee5bc3c20c8190bf2cf272f4170e95 completed April 26, 2026, 6:38 p.m.
NER Named-entity recognition batch_69f60be30d14819088ccd8f162874346 completed May 2, 2026, 2:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a118f987ab881909b7ad7deeee40884 completed May 23, 2026, 11:29 a.m.
NEDg Description generation batch_6a11902e8fa08190a631fab5541f89ca completed May 23, 2026, 11:31 a.m.
NED2 Entity disambiguation (via description) batch_6a119094eaf88190a68b09d1ec79b634 completed May 23, 2026, 11:33 a.m.
Created at: April 26, 2026, 8:19 p.m.