Triple
T14567251
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Funny Games U.S. |
E341815
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Christian Baute
Christian Baute is a film producer known for his work on the psychological thriller "Funny Games U.S."
|
E1106755
|
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: Christian Baute | Statement: [Funny Games U.S., producer, Christian Baute]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Christian Baute Context triple: [Funny Games U.S., producer, Christian Baute]
-
A.
Blake Schilb
Blake Schilb is an American-born professional basketball player, naturalized Czech, known for his versatile wing play in European leagues and appearances with the Czech national team.
-
B.
Michael Mertens
Michael Mertens is a writer known for his work on the character Dr. Mabuse.
-
C.
Michael Mertens
Michael Mertens is a member of the German industrial metal band Propaganda.
-
D.
Benny Hollinger
Benny Hollinger was a Canadian prospector best known for his pivotal role in discovering the rich gold deposits that led to the development of the Porcupine mining camp in Ontario.
-
E.
David Checel
David Checel is a film editor known for his work on major Hollywood productions, including the 2010 action film "The Losers."
- 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: Christian Baute Triple: [Funny Games U.S., producer, Christian Baute]
Generated description
Christian Baute is a film producer known for his work on the psychological thriller "Funny Games U.S."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Christian Baute Target entity description: Christian Baute is a film producer known for his work on the psychological thriller "Funny Games U.S."
-
A.
Blake Schilb
Blake Schilb is an American-born professional basketball player, naturalized Czech, known for his versatile wing play in European leagues and appearances with the Czech national team.
-
B.
Michael Mertens
Michael Mertens is a writer known for his work on the character Dr. Mabuse.
-
C.
Michael Mertens
Michael Mertens is a member of the German industrial metal band Propaganda.
-
D.
Benny Hollinger
Benny Hollinger was a Canadian prospector best known for his pivotal role in discovering the rich gold deposits that led to the development of the Porcupine mining camp in Ontario.
-
E.
David Checel
David Checel is a film editor known for his work on major Hollywood productions, including the 2010 action film "The Losers."
- 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_69d822dcc6248190bed689984bceb0e2 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb38d89fc819086709fd3607b835f |
completed | April 14, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8ac669cc819083e05620b1e8c370 |
completed | May 8, 2026, 7:03 a.m. |
| NEDg | Description generation | batch_69fd8c5b09448190ad084746a6dd23f5 |
completed | May 8, 2026, 7:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd8d609684819090a9c3f2304f4a6a |
completed | May 8, 2026, 7:14 a.m. |
Created at: April 10, 2026, 1:23 a.m.