Triple
T20995047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murder by Numbers |
E517124
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Sam Kennedy
Sam Kennedy is the central detective protagonist in the 2002 crime thriller film "Murder by Numbers," investigating a meticulously planned murder committed by two high school students.
|
E1460838
|
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: Sam Kennedy | Statement: [Murder by Numbers, mainCharacter, Sam Kennedy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sam Kennedy Context triple: [Murder by Numbers, mainCharacter, Sam Kennedy]
-
A.
Sam Kennedy
Sam Kennedy is a Major League Baseball executive best known for serving as the president and CEO of the Boston Red Sox.
-
B.
Ryan Kennedy
Ryan Kennedy is a Canadian actor known for his roles in television series such as the crime drama "Tin Star."
-
C.
Steve Kennedy
Steve Kennedy is a theatrical sound designer known for his work on major stage productions, including the Broadway musical "On Your Feet!".
-
D.
Tom Kennedy
Tom Kennedy is an actor known for his role in the film "The Big House."
-
E.
Tom Kennedy
Tom Kennedy is an American jazz and fusion bassist renowned for his virtuosic technique and collaborations with leading contemporary jazz artists.
- 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: Sam Kennedy Triple: [Murder by Numbers, mainCharacter, Sam Kennedy]
Generated description
Sam Kennedy is the central detective protagonist in the 2002 crime thriller film "Murder by Numbers," investigating a meticulously planned murder committed by two high school students.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sam Kennedy Target entity description: Sam Kennedy is the central detective protagonist in the 2002 crime thriller film "Murder by Numbers," investigating a meticulously planned murder committed by two high school students.
-
A.
Sam Kennedy
Sam Kennedy is a Major League Baseball executive best known for serving as the president and CEO of the Boston Red Sox.
-
B.
Ryan Kennedy
Ryan Kennedy is a Canadian actor known for his roles in television series such as the crime drama "Tin Star."
-
C.
Steve Kennedy
Steve Kennedy is a theatrical sound designer known for his work on major stage productions, including the Broadway musical "On Your Feet!".
-
D.
Tom Kennedy
Tom Kennedy is an actor known for his role in the film "The Big House."
-
E.
Tom Kennedy
Tom Kennedy is an American jazz and fusion bassist renowned for his virtuosic technique and collaborations with leading contemporary jazz artists.
- 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_69e0b5006e2881909fc2383f841740cc |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc1fd5d48190a56981cee95ebd69 |
completed | April 21, 2026, 4:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a092fcdc8648190b92b03d417918883 |
completed | May 17, 2026, 3:02 a.m. |
| NEDg | Description generation | batch_6a0930a5cffc8190a7d1cc19e5cacf49 |
completed | May 17, 2026, 3:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a093133c77c8190a9e0326980c7717f |
completed | May 17, 2026, 3:08 a.m. |
Created at: April 16, 2026, 1:50 p.m.