Triple
T11993267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blanke |
E285464
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Uwe Blanke
Uwe Blanke is a person notable enough to be recognized as a namesake of the surname Blanke.
|
E1040113
|
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: Uwe Blanke | Statement: [Blanke, hasNotableBearer, Uwe Blanke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uwe Blanke Context triple: [Blanke, hasNotableBearer, Uwe Blanke]
-
A.
Uwe Thiel
Uwe Thiel is a relatively obscure individual primarily known only as a namesake of the surname Thiel, with no widely recognized public achievements or roles.
-
B.
Klaus Busse
Klaus Busse is a German automotive designer known for leading design at Maserati and other Stellantis brands, shaping the look of several modern luxury and performance vehicles.
-
C.
Udo Cropa
Udo Cropa is a character in the crime drama film "Dinner Rush," set in a high-end New York City restaurant.
-
D.
Uwe Krupp
Uwe Krupp is a former German professional ice hockey defenseman and coach, best known for his NHL career and for scoring the Stanley Cup–winning goal for the Colorado Avalanche in 1996.
-
E.
Jürgen Knieper
Jürgen Knieper is a German composer best known for his film and television scores, including work on notable German productions and international art-house films.
- 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: Uwe Blanke Triple: [Blanke, hasNotableBearer, Uwe Blanke]
Generated description
Uwe Blanke is a person notable enough to be recognized as a namesake of the surname Blanke.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Uwe Blanke Target entity description: Uwe Blanke is a person notable enough to be recognized as a namesake of the surname Blanke.
-
A.
Uwe Thiel
Uwe Thiel is a relatively obscure individual primarily known only as a namesake of the surname Thiel, with no widely recognized public achievements or roles.
-
B.
Klaus Busse
Klaus Busse is a German automotive designer known for leading design at Maserati and other Stellantis brands, shaping the look of several modern luxury and performance vehicles.
-
C.
Udo Cropa
Udo Cropa is a character in the crime drama film "Dinner Rush," set in a high-end New York City restaurant.
-
D.
Uwe Krupp
Uwe Krupp is a former German professional ice hockey defenseman and coach, best known for his NHL career and for scoring the Stanley Cup–winning goal for the Colorado Avalanche in 1996.
-
E.
Jürgen Knieper
Jürgen Knieper is a German composer best known for his film and television scores, including work on notable German productions and international art-house films.
- 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903b11ac481909866b611380792e7 |
completed | April 10, 2026, 2:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f739644ed48190896d7b2b6c3b5f8f |
completed | May 3, 2026, 12:02 p.m. |
| NEDg | Description generation | batch_69f73add05c881908e65cb157a0a5fc3 |
completed | May 3, 2026, 12:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f73b434a0881909f1f75cad643cb70 |
completed | May 3, 2026, 12:10 p.m. |
Created at: April 8, 2026, 9:46 p.m.