Triple
T16760966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 633 Squadron (1964 film) |
E407341
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Maria Perschy
Maria Perschy was an Austrian actress known for her roles in European and Hollywood films during the 1950s and 1960s.
|
E1236434
|
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: Maria Perschy | Statement: [633 Squadron (1964 film), castMember, Maria Perschy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maria Perschy Context triple: [633 Squadron (1964 film), castMember, Maria Perschy]
-
A.
Marta Linden
Marta Linden was an American film actress active in the 1930s and 1940s, known for supporting roles in Hollywood studio productions.
-
B.
Laura Perens
Laura Perens is known as the spouse of open-source software advocate and Debian co-founder Bruce Perens.
-
C.
Anna Jachthuber
Anna Jachthuber was the wife of William S. Harley, co-founder of the Harley-Davidson Motor Company.
-
D.
Nicole Kruspe
Nicole Kruspe is a linguist known for her extensive research and documentation of Aslian languages spoken by indigenous communities in the Malay Peninsula.
-
E.
Alisa Lepselter
Alisa Lepselter is an American film editor best known for her long-time collaboration with director Woody Allen on numerous critically acclaimed films.
- 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: Maria Perschy Triple: [633 Squadron (1964 film), castMember, Maria Perschy]
Generated description
Maria Perschy was an Austrian actress known for her roles in European and Hollywood films during the 1950s and 1960s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maria Perschy Target entity description: Maria Perschy was an Austrian actress known for her roles in European and Hollywood films during the 1950s and 1960s.
-
A.
Marta Linden
Marta Linden was an American film actress active in the 1930s and 1940s, known for supporting roles in Hollywood studio productions.
-
B.
Laura Perens
Laura Perens is known as the spouse of open-source software advocate and Debian co-founder Bruce Perens.
-
C.
Anna Jachthuber
Anna Jachthuber was the wife of William S. Harley, co-founder of the Harley-Davidson Motor Company.
-
D.
Nicole Kruspe
Nicole Kruspe is a linguist known for her extensive research and documentation of Aslian languages spoken by indigenous communities in the Malay Peninsula.
-
E.
Alisa Lepselter
Alisa Lepselter is an American film editor best known for her long-time collaboration with director Woody Allen on numerous critically acclaimed films.
- 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_69d8839174188190909f190097207065 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3abec638c81909d71ff452a4123c9 |
completed | April 18, 2026, 4:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00bb069cf481908e029b26ad96d3b5 |
completed | May 10, 2026, 5:06 p.m. |
| NEDg | Description generation | batch_6a00bc136bfc8190ab93cd8e0e7eaf1c |
completed | May 10, 2026, 5:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00bca0a3808190be3d1d7ebd77cc20 |
completed | May 10, 2026, 5:13 p.m. |
Created at: April 10, 2026, 5:21 a.m.