Triple
T7386864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sauter |
E170402
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Timo Sauter
Timo Sauter is an individual notable enough to be recognized as a bearer of the surname Sauter, though specific widely known public information about him is limited.
|
E660905
|
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: Timo Sauter | Statement: [Sauter, hasNotableBearer, Timo Sauter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Timo Sauter Context triple: [Sauter, hasNotableBearer, Timo Sauter]
-
A.
Kai Wiesinger
Kai Wiesinger is a German actor known for his roles in film and television, often appearing in historical dramas and popular German cinema.
-
B.
Stephan Sauer
Stephan Sauer is a notable individual who shares the surname Sauer and is recognized for achievements significant enough to be specifically referenced.
-
C.
Sebastian Knapp
Sebastian Knapp is an actor best known for his role in the 2013 television miniseries adaptation of the Bible.
-
D.
Sven Wagner
Sven Wagner is a German local politician who serves as the mayor of the town of Aschersleben in Saxony-Anhalt.
-
E.
Sebastian Rudolph
Sebastian Rudolph is a German actor known for his work in film, television, and theater, including roles in historical and dramatic productions.
- 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: Timo Sauter Triple: [Sauter, hasNotableBearer, Timo Sauter]
Generated description
Timo Sauter is an individual notable enough to be recognized as a bearer of the surname Sauter, though specific widely known public information about him is limited.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Timo Sauter Target entity description: Timo Sauter is an individual notable enough to be recognized as a bearer of the surname Sauter, though specific widely known public information about him is limited.
-
A.
Kai Wiesinger
Kai Wiesinger is a German actor known for his roles in film and television, often appearing in historical dramas and popular German cinema.
-
B.
Stephan Sauer
Stephan Sauer is a notable individual who shares the surname Sauer and is recognized for achievements significant enough to be specifically referenced.
-
C.
Sebastian Knapp
Sebastian Knapp is an actor best known for his role in the 2013 television miniseries adaptation of the Bible.
-
D.
Sven Wagner
Sven Wagner is a German local politician who serves as the mayor of the town of Aschersleben in Saxony-Anhalt.
-
E.
Sebastian Rudolph
Sebastian Rudolph is a German actor known for his work in film, television, and theater, including roles in historical and dramatic productions.
- 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_69c68a5e2c9081909e713ce866e0060a |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1f2bac481908ac74069182a4ce4 |
completed | March 27, 2026, 9:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c802e56fb48190976612d2a94d6ee5 |
completed | March 28, 2026, 4:33 p.m. |
| NEDg | Description generation | batch_69c803707cec8190bb474c959ef93d48 |
completed | March 28, 2026, 4:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c803ed9ec4819090a9481954060769 |
completed | March 28, 2026, 4:38 p.m. |
Created at: March 27, 2026, 3:08 p.m.