Triple
T9472367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sundern |
E228422
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Katrin Brenner
Katrin Brenner is a German local politician who serves as the mayor of the town of Sundern in North Rhine-Westphalia.
|
E811165
|
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: Katrin Brenner | Statement: [Sundern, hasMayor, Katrin Brenner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Katrin Brenner Context triple: [Sundern, hasMayor, Katrin Brenner]
-
A.
Katrin Houben
Katrin Houben is an individual notable enough to be recognized as a namesake or prominent bearer of the surname Houben.
-
B.
Sabine Völker
Sabine Völker is a German speed skater known for winning an Olympic bronze medal in the 500 m event at the 2002 Winter Games.
-
C.
Angelika Dittrich
Angelika Dittrich was the third wife of the famous Austrian composer Johann Strauss II.
-
D.
Ute Grunert
Ute Grunert is known as the spouse of Nobel Prize–winning German author Günter Grass.
-
E.
Verena Becker
Verena Becker is a former member of the German left-wing militant group Movement 2 June, known for her involvement in political violence and subsequent legal proceedings in the 1970s and 1980s.
- 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: Katrin Brenner Triple: [Sundern, hasMayor, Katrin Brenner]
Generated description
Katrin Brenner is a German local politician who serves as the mayor of the town of Sundern in North Rhine-Westphalia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Katrin Brenner Target entity description: Katrin Brenner is a German local politician who serves as the mayor of the town of Sundern in North Rhine-Westphalia.
-
A.
Katrin Houben
Katrin Houben is an individual notable enough to be recognized as a namesake or prominent bearer of the surname Houben.
-
B.
Sabine Völker
Sabine Völker is a German speed skater known for winning an Olympic bronze medal in the 500 m event at the 2002 Winter Games.
-
C.
Angelika Dittrich
Angelika Dittrich was the third wife of the famous Austrian composer Johann Strauss II.
-
D.
Ute Grunert
Ute Grunert is known as the spouse of Nobel Prize–winning German author Günter Grass.
-
E.
Verena Becker
Verena Becker is a former member of the German left-wing militant group Movement 2 June, known for her involvement in political violence and subsequent legal proceedings in the 1970s and 1980s.
- 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_69ca847162c48190b079076c9595513c |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7fef6f288190b2d158c829b31de9 |
completed | April 1, 2026, 8:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1820ba67881909955c4198c7289b1 |
completed | April 4, 2026, 9:26 p.m. |
| NEDg | Description generation | batch_69d182b613348190917338a8f0b896bb |
completed | April 4, 2026, 9:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1835c916081908e3e1bb1e5499424 |
completed | April 4, 2026, 9:32 p.m. |
Created at: March 30, 2026, 7:54 p.m.