Triple
T10603370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trassenheide |
E275806
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Michael Koch
Michael Koch is a German local politician who serves as the mayor of the Baltic Sea resort municipality of Trassenheide.
|
E874593
|
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: Michael Koch | Statement: [Trassenheide, hasMayor, Michael Koch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Koch Context triple: [Trassenheide, hasMayor, Michael Koch]
-
A.
Christian Roth
Christian Roth was a mountaineer known for participating in the first recorded ascent of Shkhara, one of the highest peaks in the Caucasus.
-
B.
Christian Meyer
Christian Meyer is the husband of American author Stephenie Meyer, best known for her Twilight series.
-
C.
Christian Kaufmann
Christian Kaufmann was a renowned Swiss mountain guide and alpinist active in the late 19th and early 20th centuries, known for numerous first ascents in the Alps and beyond.
-
D.
Christian Heinecken
Christian Heinecken was a notable individual interred at Glenwood Cemetery in Washington, D.C., recognized for his historical significance to the area.
-
E.
Philip Schulz-Deyle
Philip Schulz-Deyle is a film producer known for his work on major feature films, including the political thriller "Snowden" (2016).
- 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: Michael Koch Triple: [Trassenheide, hasMayor, Michael Koch]
Generated description
Michael Koch is a German local politician who serves as the mayor of the Baltic Sea resort municipality of Trassenheide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Koch Target entity description: Michael Koch is a German local politician who serves as the mayor of the Baltic Sea resort municipality of Trassenheide.
-
A.
Christian Roth
Christian Roth was a mountaineer known for participating in the first recorded ascent of Shkhara, one of the highest peaks in the Caucasus.
-
B.
Christian Meyer
Christian Meyer is the husband of American author Stephenie Meyer, best known for her Twilight series.
-
C.
Christian Kaufmann
Christian Kaufmann was a renowned Swiss mountain guide and alpinist active in the late 19th and early 20th centuries, known for numerous first ascents in the Alps and beyond.
-
D.
Christian Heinecken
Christian Heinecken was a notable individual interred at Glenwood Cemetery in Washington, D.C., recognized for his historical significance to the area.
-
E.
Philip Schulz-Deyle
Philip Schulz-Deyle is a film producer known for his work on major feature films, including the political thriller "Snowden" (2016).
- 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6ded6d698819084f96f46ea941461 |
completed | April 8, 2026, 11:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d95eaffcd0819098e0a06a731b602f |
completed | April 10, 2026, 8:33 p.m. |
| NEDg | Description generation | batch_69d961aaf71881908289244e0a490492 |
completed | April 10, 2026, 8:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d9623cf54081908abcdc88e13d5176 |
completed | April 10, 2026, 8:49 p.m. |
Created at: April 8, 2026, 7:32 p.m.