Triple
T12454209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bangert |
E297611
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Jansen Bangert
Jansen Bangert is an individual notable enough to be recognized as a prominent bearer of the surname Bangert.
|
E983702
|
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: Jansen Bangert | Statement: [Bangert, hasNotableBearer, Jansen Bangert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jansen Bangert Context triple: [Bangert, hasNotableBearer, Jansen Bangert]
-
A.
Gui Bonsiepe
Gui Bonsiepe is a German designer, design theorist, and educator known for his influential work in interface and information design and his contributions to design education, particularly in Latin America.
-
B.
Jan D'Alquen
Jan D'Alquen is a cinematographer best known for his work on the classic coming-of-age film "American Graffiti."
-
C.
Emmy Sonnemann
Emmy Sonnemann was a German stage actress best known as the second wife of Hermann Göring, a leading figure in Nazi Germany.
-
D.
Jan Schaeferbrug
Jan Schaeferbrug is a bridge in Amsterdam that links the former docklands area, including KNSM Island, with the city’s eastern waterfront.
-
E.
Jan Leike
Jan Leike is an AI researcher known for his work on AI safety and alignment, including influential contributions at DeepMind and OpenAI.
- 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: Jansen Bangert Triple: [Bangert, hasNotableBearer, Jansen Bangert]
Generated description
Jansen Bangert is an individual notable enough to be recognized as a prominent bearer of the surname Bangert.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jansen Bangert Target entity description: Jansen Bangert is an individual notable enough to be recognized as a prominent bearer of the surname Bangert.
-
A.
Gui Bonsiepe
Gui Bonsiepe is a German designer, design theorist, and educator known for his influential work in interface and information design and his contributions to design education, particularly in Latin America.
-
B.
Jan D'Alquen
Jan D'Alquen is a cinematographer best known for his work on the classic coming-of-age film "American Graffiti."
-
C.
Emmy Sonnemann
Emmy Sonnemann was a German stage actress best known as the second wife of Hermann Göring, a leading figure in Nazi Germany.
-
D.
Jan Schaeferbrug
Jan Schaeferbrug is a bridge in Amsterdam that links the former docklands area, including KNSM Island, with the city’s eastern waterfront.
-
E.
Jan Leike
Jan Leike is an AI researcher known for his work on AI safety and alignment, including influential contributions at DeepMind and OpenAI.
- 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_69d6ada166c48190b902972cd2408fa3 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94da0b5988190b9df26dd3bb87337 |
completed | April 10, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63f190c788190adceaab8117d52a6 |
completed | May 2, 2026, 6:14 p.m. |
| NEDg | Description generation | batch_69f6405f9f6481909bcc3b2e3deeae7e |
completed | May 2, 2026, 6:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f64168d23881908daee7d7cba2160d |
completed | May 2, 2026, 6:24 p.m. |
Created at: April 8, 2026, 9:56 p.m.