Triple
T10738617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Männerpension |
E253260
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Gode Benedix
Gode Benedix is a German actor known for his role in the comedy film "Männerpension."
|
E883191
|
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: Gode Benedix | Statement: [Männerpension, hasCastMember, Gode Benedix]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gode Benedix Context triple: [Männerpension, hasCastMember, Gode Benedix]
-
A.
Jeremy Hansen
Jeremy Hansen is a Canadian Space Agency astronaut and former Royal Canadian Air Force fighter pilot selected to fly on NASA’s Artemis II mission, the first crewed lunar voyage of the Artemis program.
-
B.
Richard Beymer
Richard Beymer is an American actor best known for playing Tony in the 1961 film adaptation of the musical "West Side Story."
-
C.
Brolin
Brolin is a surname most prominently associated with American actor Josh Brolin and his family.
-
D.
Mac Ahlberg
Mac Ahlberg was a Swedish cinematographer and film director known for his work on numerous genre and Hollywood films, including major studio comedies and action movies.
-
E.
Wil Bakula
Wil Bakula is one of the children of American actor Scott Bakula, known for his work in television and film.
- 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: Gode Benedix Triple: [Männerpension, hasCastMember, Gode Benedix]
Generated description
Gode Benedix is a German actor known for his role in the comedy film "Männerpension."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gode Benedix Target entity description: Gode Benedix is a German actor known for his role in the comedy film "Männerpension."
-
A.
Jeremy Hansen
Jeremy Hansen is a Canadian Space Agency astronaut and former Royal Canadian Air Force fighter pilot selected to fly on NASA’s Artemis II mission, the first crewed lunar voyage of the Artemis program.
-
B.
Richard Beymer
Richard Beymer is an American actor best known for playing Tony in the 1961 film adaptation of the musical "West Side Story."
-
C.
Brolin
Brolin is a surname most prominently associated with American actor Josh Brolin and his family.
-
D.
Mac Ahlberg
Mac Ahlberg was a Swedish cinematographer and film director known for his work on numerous genre and Hollywood films, including major studio comedies and action movies.
-
E.
Wil Bakula
Wil Bakula is one of the children of American actor Scott Bakula, known for his work in television and film.
- 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_69de22ed6edc8190beb76bd2971c7cec |
completed | April 14, 2026, 11:20 a.m. |
| NEDg | Description generation | batch_69de25d25474819081402b75ef7492f6 |
completed | April 14, 2026, 11:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69de2808244c8190bdb2d4d49f30e0d7 |
completed | April 14, 2026, 11:42 a.m. |
Created at: April 8, 2026, 9:14 p.m.