Triple

T8020362
Position Surface form Disambiguated ID Type / Status
Subject Unstrut River region E186724 entity
Predicate contains P35 FINISHED
Object Sömmerda
Sömmerda is a town in the German state of Thuringia, known historically for its industrial development and location on the Unstrut River.
 E722431 NE FINISHED

How this triple was built (4 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: Sömmerda | Statement: [Unstrut River region, contains, Sömmerda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sömmerda
Context triple: [Unstrut River region, contains, Sömmerda]
  • A. Trakehnen
    Trakehnen was a renowned East Prussian stud farm and village, historically famous as the cradle of the Trakehner horse breed.
  • B. Haldensleben
    Haldensleben is a town in the German state of Saxony-Anhalt, known as an administrative and economic center with historical roots dating back to the Middle Ages.
  • C. Rudolstadt
    Rudolstadt is a historic town in the German state of Thuringia, known for its picturesque old town, Heidecksburg Castle, and cultural festivals.
  • D. Zerbst
    Zerbst is a historic town in Saxony-Anhalt, Germany, known as the birthplace of Catherine the Great and for its former role as a princely residence.
  • E. Weiterstadt
    Weiterstadt is a town in the German state of Hesse, located near Darmstadt and known for its residential areas and commercial centers.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Sömmerda
Triple: [Unstrut River region, contains, Sömmerda]
Generated description
Sömmerda is a town in the German state of Thuringia, known historically for its industrial development and location on the Unstrut River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sömmerda
Target entity description: Sömmerda is a town in the German state of Thuringia, known historically for its industrial development and location on the Unstrut River.
  • A. Trakehnen
    Trakehnen was a renowned East Prussian stud farm and village, historically famous as the cradle of the Trakehner horse breed.
  • B. Haldensleben
    Haldensleben is a town in the German state of Saxony-Anhalt, known as an administrative and economic center with historical roots dating back to the Middle Ages.
  • C. Rudolstadt
    Rudolstadt is a historic town in the German state of Thuringia, known for its picturesque old town, Heidecksburg Castle, and cultural festivals.
  • D. Zerbst
    Zerbst is a historic town in Saxony-Anhalt, Germany, known as the birthplace of Catherine the Great and for its former role as a princely residence.
  • E. Weiterstadt
    Weiterstadt is a town in the German state of Hesse, located near Darmstadt and known for its residential areas and commercial centers.
  • F. None of above. chosen

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_69ca82ac7fc081909b1398cf025423af completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3e8d90488190b57d1e748e272061 completed March 31, 2026, 3:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd6759e83c8190869732f955279cee completed April 1, 2026, 6:43 p.m.
NEDg Description generation batch_69cd6d4fa17481909f28ad7eb9bceb42 completed April 1, 2026, 7:09 p.m.
NED2 Entity disambiguation (via description) batch_69cd7da4f3a0819080eed3d03c293789 completed April 1, 2026, 8:18 p.m.
Created at: March 30, 2026, 5:20 p.m.