Triple
T8800612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jochen Zeitz |
E209396
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Jochen
Jochen is a masculine given name of German origin commonly used in German-speaking countries.
|
E759294
|
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: Jochen | Statement: [Jochen Zeitz, givenName, Jochen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jochen Context triple: [Jochen Zeitz, givenName, Jochen]
-
A.
Jochen Nickel
Jochen Nickel is a German actor known for his character roles in films and television, including appearances in notable World War II dramas.
-
B.
Jochen Hecht
Jochen Hecht is a German former professional ice hockey forward who enjoyed a long NHL career, notably with the Buffalo Sabres, and represented Germany in multiple international tournaments.
-
C.
Sebastian Rudolph
Sebastian Rudolph is a German actor known for his work in film, television, and theater, including roles in historical and dramatic productions.
-
D.
Andreas Scholz
Andreas Scholz is a person known for bearing the surname Scholz, though no widely recognized public profile or specific notable achievements are clearly associated with him from the given information.
-
E.
Sven Wagner
Sven Wagner is a German local politician who serves as the mayor of the town of Aschersleben in Saxony-Anhalt.
- 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: Jochen Triple: [Jochen Zeitz, givenName, Jochen]
Generated description
Jochen is a masculine given name of German origin commonly used in German-speaking countries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jochen Target entity description: Jochen is a masculine given name of German origin commonly used in German-speaking countries.
-
A.
Jochen Nickel
Jochen Nickel is a German actor known for his character roles in films and television, including appearances in notable World War II dramas.
-
B.
Jochen Hecht
Jochen Hecht is a German former professional ice hockey forward who enjoyed a long NHL career, notably with the Buffalo Sabres, and represented Germany in multiple international tournaments.
-
C.
Sebastian Rudolph
Sebastian Rudolph is a German actor known for his work in film, television, and theater, including roles in historical and dramatic productions.
-
D.
Andreas Scholz
Andreas Scholz is a person known for bearing the surname Scholz, though no widely recognized public profile or specific notable achievements are clearly associated with him from the given information.
-
E.
Sven Wagner
Sven Wagner is a German local politician who serves as the mayor of the town of Aschersleben in Saxony-Anhalt.
- 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_69ca836320e48190b5cf585b90a322c4 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5fb8aab88190befed16301e08efc |
completed | March 31, 2026, 11:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf6f6fdd688190bf40bbde0be991e1 |
completed | April 3, 2026, 7:42 a.m. |
| NEDg | Description generation | batch_69cf718a6f2c81908f8b8d08a1437749 |
completed | April 3, 2026, 7:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf7275fea08190b8999fb30663ff17 |
completed | April 3, 2026, 7:55 a.m. |
Created at: March 30, 2026, 6:44 p.m.