Triple
T1408163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Boondock Saints |
E31743
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Bill DeRonde
Bill DeRonde is a film editor best known for his work on the cult action film "The Boondock Saints."
|
E255692
|
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: Bill DeRonde | Statement: [The Boondock Saints, editedBy, Bill DeRonde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bill DeRonde Context triple: [The Boondock Saints, editedBy, Bill DeRonde]
-
A.
Jerry Dandrige
Jerry Dandrige is the charismatic yet sinister vampire antagonist in the 2011 horror-comedy film "Fright Night."
-
B.
Dale Tremont
Dale Tremont is the glamorous and witty fashion model portrayed by Ginger Rogers in the 1935 musical film "Top Hat."
-
C.
Dale Miller
Dale Miller is a prominent logician and computer scientist known for his influential work in proof theory, logic programming, and automated reasoning.
-
D.
Bill Radke
Bill Radke is an American radio host, comedian, and journalist best known for his work on public radio programs such as KUOW’s "Week in Review" and the former NPR show "Marketplace Morning Report."
-
E.
Dennis Burkley
Dennis Burkley was an American character actor known for his burly appearance and roles in numerous film and television productions from the 1970s through the early 2000s.
- 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: Bill DeRonde Triple: [The Boondock Saints, editedBy, Bill DeRonde]
Generated description
Bill DeRonde is a film editor best known for his work on the cult action film "The Boondock Saints."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bill DeRonde Target entity description: Bill DeRonde is a film editor best known for his work on the cult action film "The Boondock Saints."
-
A.
Jerry Dandrige
Jerry Dandrige is the charismatic yet sinister vampire antagonist in the 2011 horror-comedy film "Fright Night."
-
B.
Dale Tremont
Dale Tremont is the glamorous and witty fashion model portrayed by Ginger Rogers in the 1935 musical film "Top Hat."
-
C.
Dale Miller
Dale Miller is a prominent logician and computer scientist known for his influential work in proof theory, logic programming, and automated reasoning.
-
D.
Bill Radke
Bill Radke is an American radio host, comedian, and journalist best known for his work on public radio programs such as KUOW’s "Week in Review" and the former NPR show "Marketplace Morning Report."
-
E.
Dennis Burkley
Dennis Burkley was an American character actor known for his burly appearance and roles in numerous film and television productions from the 1970s through the early 2000s.
- 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_69a49918e1f88190ba610f9dc8114578 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c3bf7f0c8190aee96818de6ff4a5 |
completed | March 1, 2026, 10:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae891d41d88190aec6987c64c99757 |
completed | March 9, 2026, 8:47 a.m. |
| NEDg | Description generation | batch_69ae8b0b27cc819099a5df60d678d3e2 |
completed | March 9, 2026, 8:55 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae8b79633881908acf94f8db389c0f |
completed | March 9, 2026, 8:57 a.m. |
Created at: March 1, 2026, 7:59 p.m.