Triple
T1855905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jochen Nickel |
E41700
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Jochen |
E41700
|
NE FINISHED |
How this triple was built (2 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 Nickel, givenName, Jochen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jochen Context triple: [Jochen Nickel, givenName, Jochen]
-
A.
Jochen Nickel
chosen
Jochen Nickel is a German actor known for his character roles in films and television, including appearances in notable World War II dramas.
-
B.
Sebastian Rudolph
Sebastian Rudolph is a German actor known for his work in film, television, and theater, including roles in historical and dramatic productions.
-
C.
Philipp Demandt
Philipp Demandt is a German art historian and museum director known for leading major cultural institutions such as the Städel Museum and the Liebieghaus Skulpturensammlung in Frankfurt.
-
D.
Jürgen
Jürgen is a masculine given name of German origin, commonly used in German-speaking countries.
-
E.
Nico Habermann
Nico Habermann was a German-American computer scientist known for his contributions to programming languages, operating systems, and software engineering, and for his influential academic leadership at Carnegie Mellon University.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a8864a83848190a4ec02721306c511 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb07e5ed48190a7b8858e2b355109 |
completed | March 7, 2026, 4:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69add1c89bdc8190acf517a7731fa5c7 |
completed | March 8, 2026, 7:45 p.m. |
Created at: March 4, 2026, 7:33 p.m.