Triple
T9214257
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sophie von Kühn |
E221202
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
von Kühn
von Kühn is a German noble family name historically associated with figures such as Sophie von Kühn.
|
E785515
|
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: von Kühn | Statement: [Sophie von Kühn, familyName, von Kühn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: von Kühn Context triple: [Sophie von Kühn, familyName, von Kühn]
-
A.
von Schlebrügge
von Schlebrügge is the aristocratic German-Swedish family name of Nena von Schlebrügge, a former fashion model and mother of actress Uma Thurman.
-
B.
Tobias Kohn
Tobias Kohn is a computer scientist and software developer known for his contributions to the Python language, including co-authoring PEP 622 on pattern matching.
-
C.
von Weichs
von Weichs is a German noble family name most prominently associated with Maximilian von Weichs, a senior Wehrmacht field marshal during World War II.
-
D.
Klaus Hamm-Brücher
Klaus Hamm-Brücher was a German academic and public figure best known as the husband and close political companion of liberal politician Hildegard Hamm-Brücher.
-
E.
Johannes Mühlenkamp
Johannes Mühlenkamp was a German Waffen-SS officer who commanded the 5th SS Panzer Division "Wiking" during World War II.
- 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: von Kühn Triple: [Sophie von Kühn, familyName, von Kühn]
Generated description
von Kühn is a German noble family name historically associated with figures such as Sophie von Kühn.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: von Kühn Target entity description: von Kühn is a German noble family name historically associated with figures such as Sophie von Kühn.
-
A.
von Schlebrügge
von Schlebrügge is the aristocratic German-Swedish family name of Nena von Schlebrügge, a former fashion model and mother of actress Uma Thurman.
-
B.
Tobias Kohn
Tobias Kohn is a computer scientist and software developer known for his contributions to the Python language, including co-authoring PEP 622 on pattern matching.
-
C.
von Weichs
von Weichs is a German noble family name most prominently associated with Maximilian von Weichs, a senior Wehrmacht field marshal during World War II.
-
D.
Klaus Hamm-Brücher
Klaus Hamm-Brücher was a German academic and public figure best known as the husband and close political companion of liberal politician Hildegard Hamm-Brücher.
-
E.
Johannes Mühlenkamp
Johannes Mühlenkamp was a German Waffen-SS officer who commanded the 5th SS Panzer Division "Wiking" during World War II.
- 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_69ca83eae42c8190a0ea9e040710a277 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccda06bf80819094c6e74b4b6a31e4 |
completed | April 1, 2026, 8:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d06613daf88190a0128fd53ea1b134 |
completed | April 4, 2026, 1:15 a.m. |
| NEDg | Description generation | batch_69d0678b89ac8190b807e1c3b457a503 |
completed | April 4, 2026, 1:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d0688d4c388190bb024b03cc86d08f |
completed | April 4, 2026, 1:25 a.m. |
Created at: March 30, 2026, 7:27 p.m.