Triple
T11467694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Munich (2005 film) |
E271818
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Hanns Zischler
Hanns Zischler is a German actor and writer known for his work in both European art-house cinema and international films.
|
E1161442
|
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: Hanns Zischler | Statement: [Munich (2005 film), castMember, Hanns Zischler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanns Zischler Context triple: [Munich (2005 film), castMember, Hanns Zischler]
-
A.
Helmut Veith
Helmut Veith was an Austrian computer scientist renowned for his contributions to logic in computer science, formal verification, and model checking.
-
B.
Franz Seldte
Franz Seldte was a German nationalist politician and co-founder of the Stahlhelm veterans' organization who became a prominent minister in Nazi Germany.
-
C.
Fritz Loerzer
Fritz Loerzer was a German military officer and World War II Luftwaffe general.
-
D.
Franz Eckert
Franz Eckert was a German musician and composer known for arranging and influencing early modern national anthems in Japan and Korea.
-
E.
Helmut Berger
Helmut Berger was an Austrian actor renowned for his intense, androgynous screen presence and iconic roles in European art cinema, particularly in the films of director Luchino Visconti.
- 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: Hanns Zischler Triple: [Munich (2005 film), castMember, Hanns Zischler]
Generated description
Hanns Zischler is a German actor and writer known for his work in both European art-house cinema and international films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hanns Zischler Target entity description: Hanns Zischler is a German actor and writer known for his work in both European art-house cinema and international films.
-
A.
Helmut Veith
Helmut Veith was an Austrian computer scientist renowned for his contributions to logic in computer science, formal verification, and model checking.
-
B.
Franz Seldte
Franz Seldte was a German nationalist politician and co-founder of the Stahlhelm veterans' organization who became a prominent minister in Nazi Germany.
-
C.
Fritz Loerzer
Fritz Loerzer was a German military officer and World War II Luftwaffe general.
-
D.
Franz Eckert
Franz Eckert was a German musician and composer known for arranging and influencing early modern national anthems in Japan and Korea.
-
E.
Helmut Berger
Helmut Berger was an Austrian actor renowned for his intense, androgynous screen presence and iconic roles in European art cinema, particularly in the films of director Luchino Visconti.
- 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_69d6aae0c8d881908a5a360c0be3242e |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f74144819094479690c8151073 |
completed | April 9, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d3551cc8190848c14c15da07dc4 |
completed | May 9, 2026, 1:57 p.m. |
| NEDg | Description generation | batch_69ff3df53f14819094c1744d45d62431 |
completed | May 9, 2026, 2 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff3eab3ec88190b206ecf1b38524c7 |
completed | May 9, 2026, 2:03 p.m. |
Created at: April 8, 2026, 9:35 p.m.