Triple

T4658133
Position Surface form Disambiguated ID Type / Status
Subject John VII, Count of Nassau-Siegen E102458 entity
Predicate residence P75 FINISHED
Object Nassau-Siegen
Nassau-Siegen was a German county in the Holy Roman Empire centered on the town of Siegen and ruled by a branch of the House of Nassau.
 E457423 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: Nassau-Siegen | Statement: [John VII, Count of Nassau-Siegen, residence, Nassau-Siegen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nassau-Siegen
Context triple: [John VII, Count of Nassau-Siegen, residence, Nassau-Siegen]
  • A. Nassau-Weilburg
    Nassau-Weilburg was a historical German county and later principality within the Holy Roman Empire, ruled by a branch of the House of Nassau.
  • B. Badenburg
    Badenburg is an ornate pavilion within Munich’s Nymphenburg Palace park, known for its richly decorated interiors and historical bathing hall.
  • C. Stolberg
    Stolberg is a historic German town in the Harz region, known for its well-preserved medieval architecture and role in early Reformation-era history.
  • D. Landsberg
    Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
  • E. Siegen
    Siegen is a city in western Germany known as the birthplace of the Baroque painter Peter Paul Rubens and for its historic mining and university traditions.
  • 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: Nassau-Siegen
Triple: [John VII, Count of Nassau-Siegen, residence, Nassau-Siegen]
Generated description
Nassau-Siegen was a German county in the Holy Roman Empire centered on the town of Siegen and ruled by a branch of the House of Nassau.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nassau-Siegen
Target entity description: Nassau-Siegen was a German county in the Holy Roman Empire centered on the town of Siegen and ruled by a branch of the House of Nassau.
  • A. Nassau-Weilburg
    Nassau-Weilburg was a historical German county and later principality within the Holy Roman Empire, ruled by a branch of the House of Nassau.
  • B. Badenburg
    Badenburg is an ornate pavilion within Munich’s Nymphenburg Palace park, known for its richly decorated interiors and historical bathing hall.
  • C. Stolberg
    Stolberg is a historic German town in the Harz region, known for its well-preserved medieval architecture and role in early Reformation-era history.
  • D. Landsberg
    Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
  • E. Siegen
    Siegen is a city in western Germany known as the birthplace of the Baroque painter Peter Paul Rubens and for its historic mining and university traditions.
  • 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_69bd43d823288190952279faa0d1d066 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd63271a548190bd9662b69a45d9a5 completed March 20, 2026, 3:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdfaf5a0988190b097ef71301aebbe completed March 21, 2026, 1:57 a.m.
NEDg Description generation batch_69bdfc0964c881909e6b98a1c8ea747f completed March 21, 2026, 2:01 a.m.
NED2 Entity disambiguation (via description) batch_69bdfce1be788190ae3418df301e5136 completed March 21, 2026, 2:05 a.m.
Created at: March 20, 2026, 1:15 p.m.