Triple
T4176615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Life of Brian |
E86491
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Brian Cohen
Brian Cohen is the hapless, mistaken-for-the-Messiah protagonist of Monty Python’s satirical film "Life of Brian."
|
E418760
|
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: Brian Cohen | Statement: [Life of Brian, mainCharacter, Brian Cohen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brian Cohen Context triple: [Life of Brian, mainCharacter, Brian Cohen]
-
A.
Bruce Cohen
Bruce Cohen is an American film and television producer best known for his work on acclaimed films such as "American Beauty" and "Silver Linings Playbook."
-
B.
Andrew Cohen
Andrew Cohen is an entrepreneur best known as a founder of the wireless technology company Qualcomm.
-
C.
Jake Cohen
Jake Cohen is known primarily as the son of Michael Cohen, the former personal attorney to Donald Trump.
-
D.
Andrew Weisblum
Andrew Weisblum is an American film editor known for his work on major feature films, including collaborations with directors like Darren Aronofsky and Wes Anderson.
-
E.
Stuart Markowitz
Stuart Markowitz is a central attorney character on the television legal drama "L.A. Law," known for his earnest demeanor and complex personal and professional relationships.
- 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: Brian Cohen Triple: [Life of Brian, mainCharacter, Brian Cohen]
Generated description
Brian Cohen is the hapless, mistaken-for-the-Messiah protagonist of Monty Python’s satirical film "Life of Brian."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Brian Cohen Target entity description: Brian Cohen is the hapless, mistaken-for-the-Messiah protagonist of Monty Python’s satirical film "Life of Brian."
-
A.
Bruce Cohen
Bruce Cohen is an American film and television producer best known for his work on acclaimed films such as "American Beauty" and "Silver Linings Playbook."
-
B.
Andrew Cohen
Andrew Cohen is an entrepreneur best known as a founder of the wireless technology company Qualcomm.
-
C.
Jake Cohen
Jake Cohen is known primarily as the son of Michael Cohen, the former personal attorney to Donald Trump.
-
D.
Andrew Weisblum
Andrew Weisblum is an American film editor known for his work on major feature films, including collaborations with directors like Darren Aronofsky and Wes Anderson.
-
E.
Stuart Markowitz
Stuart Markowitz is a central attorney character on the television legal drama "L.A. Law," known for his earnest demeanor and complex personal and professional relationships.
- 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_69aed93de98c8190ad838ce507b77c8a |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af02eaa0d08190a3b805c64ef76a0c |
completed | March 9, 2026, 5:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b57f564c9c8190bfc321c8ec2dac14 |
completed | March 14, 2026, 3:31 p.m. |
| NEDg | Description generation | batch_69b58330b1d48190a3af96d3c0e7aa1b |
completed | March 14, 2026, 3:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b583ba1fd8819092b7fe73a17dc406 |
completed | March 14, 2026, 3:50 p.m. |
Created at: March 9, 2026, 3:45 p.m.