Triple
T19002147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agata Kornhauser-Duda |
E464979
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | Julian Kornhauser |
—
|
NE NERFINISHED |
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: Julian Kornhauser | Statement: [Agata Kornhauser-Duda, relative, Julian Kornhauser]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Julian Kornhauser Context triple: [Agata Kornhauser-Duda, relative, Julian Kornhauser]
-
A.
Julian Kornhauser
chosen
Julian Kornhauser is a Polish poet, literary critic, and translator associated with the New Wave (Nowa Fala) movement.
-
B.
Benjamin Kasulke
Benjamin Kasulke is an American cinematographer known for his work on independent films and collaborations with prominent indie directors.
-
C.
Matthias Grunsky
Matthias Grunsky is an Austrian cinematographer known for his long-time collaboration with director Andrew Bujalski on acclaimed independent films.
-
D.
Daniel Kurth
Daniel Kurth is a German local politician who serves as the mayor of the municipality of Panketal in the state of Brandenburg.
-
E.
Martin Kosleck
Martin Kosleck was a German-American character actor best known for playing suave villains and Nazi antagonists in Hollywood films of the 1930s and 1940s.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dd01a56c81909694a128c66b21d7 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d687cb2081909bf3ac761e292f22 |
completed | April 20, 2026, 7:32 a.m. |
Created at: April 10, 2026, 12:01 p.m.