Triple
T10738641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Männerpension |
E253260
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Anke Engelke
Anke Engelke is a prominent German comedian, actress, and television presenter known for her work in sketch comedy, film, and voice acting.
|
E893119
|
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: Anke Engelke | Statement: [Männerpension, hasCastMember, Anke Engelke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anke Engelke Context triple: [Männerpension, hasCastMember, Anke Engelke]
-
A.
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.
-
B.
Katrin Houben
Katrin Houben is an individual notable enough to be recognized as a namesake or prominent bearer of the surname Houben.
-
C.
Katrin Brenner
Katrin Brenner is a German local politician who serves as the mayor of the town of Sundern in North Rhine-Westphalia.
-
D.
Carolin Emcke
Carolin Emcke is a German journalist, author, and public intellectual known for her writings on violence, human rights, and social justice.
-
E.
Heike Drechsler
Heike Drechsler is a German former track and field athlete best known as one of history’s greatest long jumpers, winning multiple Olympic and World Championship titles.
- 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: Anke Engelke Triple: [Männerpension, hasCastMember, Anke Engelke]
Generated description
Anke Engelke is a prominent German comedian, actress, and television presenter known for her work in sketch comedy, film, and voice acting.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anke Engelke Target entity description: Anke Engelke is a prominent German comedian, actress, and television presenter known for her work in sketch comedy, film, and voice acting.
-
A.
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.
-
B.
Katrin Houben
Katrin Houben is an individual notable enough to be recognized as a namesake or prominent bearer of the surname Houben.
-
C.
Katrin Brenner
Katrin Brenner is a German local politician who serves as the mayor of the town of Sundern in North Rhine-Westphalia.
-
D.
Carolin Emcke
Carolin Emcke is a German journalist, author, and public intellectual known for her writings on violence, human rights, and social justice.
-
E.
Heike Drechsler
Heike Drechsler is a German former track and field athlete best known as one of history’s greatest long jumpers, winning multiple Olympic and World Championship titles.
- 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_69d6aa5e51e8819095f06881cecf152e |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d710424d8c81908ee9b59d622f2af5 |
completed | April 9, 2026, 2:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e2162f1f648190b325c7e7647b543e |
completed | April 17, 2026, 11:14 a.m. |
| NEDg | Description generation | batch_69e21d860d288190855ffbe60df50df9 |
completed | April 17, 2026, 11:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e21f09be508190a7c497a7680cb59e |
completed | April 17, 2026, 11:52 a.m. |
Created at: April 8, 2026, 9:14 p.m.