Triple
T2022244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 300 |
E44130
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object |
Vincent Regan
Vincent Regan is a British actor known for his roles in historical and action films such as "300," "Troy," and "Clash of the Titans."
|
E251964
|
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: Vincent Regan | Statement: [300, portrayedBy, Vincent Regan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vincent Regan Context triple: [300, portrayedBy, Vincent Regan]
-
A.
Andrew Duggan
Andrew Duggan was an American character actor known for his prolific work in film and television from the 1950s through the 1980s.
-
B.
Vincent Viola
Vincent Viola is an American billionaire businessman, former U.S. Army officer, and founder of Virtu Financial who owns the NHL’s Florida Panthers.
-
C.
Vincent Gaddis
Vincent Gaddis was an American writer and researcher best known for coining and popularizing the modern mystery surrounding the Bermuda Triangle in the mid-20th century.
-
D.
Michael McCusker
Michael McCusker is an American film editor known for his work on major Hollywood productions, including the thriller "The Girl on the Train" (2016).
-
E.
Daniel Neeson
Daniel Neeson is the son of acclaimed Irish actor Liam Neeson and his late wife, actress Natasha Richardson.
- 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: Vincent Regan Triple: [300, portrayedBy, Vincent Regan]
Generated description
Vincent Regan is a British actor known for his roles in historical and action films such as "300," "Troy," and "Clash of the Titans."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vincent Regan Target entity description: Vincent Regan is a British actor known for his roles in historical and action films such as "300," "Troy," and "Clash of the Titans."
-
A.
Andrew Duggan
Andrew Duggan was an American character actor known for his prolific work in film and television from the 1950s through the 1980s.
-
B.
Vincent Viola
Vincent Viola is an American billionaire businessman, former U.S. Army officer, and founder of Virtu Financial who owns the NHL’s Florida Panthers.
-
C.
Vincent Gaddis
Vincent Gaddis was an American writer and researcher best known for coining and popularizing the modern mystery surrounding the Bermuda Triangle in the mid-20th century.
-
D.
Michael McCusker
Michael McCusker is an American film editor known for his work on major Hollywood productions, including the thriller "The Girl on the Train" (2016).
-
E.
Daniel Neeson
Daniel Neeson is the son of acclaimed Irish actor Liam Neeson and his late wife, actress Natasha Richardson.
- 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_69a8891201bc8190aca837be6de41579 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb8efbe148190901d3650aa60408a |
completed | March 7, 2026, 5:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae7ee977c08190a90355f510aa28f2 |
completed | March 9, 2026, 8:03 a.m. |
| NEDg | Description generation | batch_69ae7f90d3a88190bf61c6f063b67c06 |
completed | March 9, 2026, 8:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae800868948190a5504969c4cabb7d |
completed | March 9, 2026, 8:08 a.m. |
Created at: March 4, 2026, 7:38 p.m.