Triple
T450176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Black Pirate |
E7108
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Sam De Grasse
Sam De Grasse was a Canadian-born silent film actor best known for his villainous roles in early Hollywood adventure and drama films.
|
E67233
|
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 De Grasse | Statement: [The Black Pirate, starring, Sam De Grasse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sam De Grasse Context triple: [The Black Pirate, starring, Sam De Grasse]
-
A.
Sam Wheeler
Sam Wheeler is the father of Ted Wheeler, the mayor of Portland, Oregon.
-
B.
Samuel Jones
Samuel Jones is an individual whose full given name is Samuel but is commonly referred to as Sam Jones.
-
C.
William Ryan
William Ryan is a marine geologist and oceanographer known for his influential work on seafloor spreading and the geological history of the Black Sea, including the "Noah's Flood" hypothesis.
-
D.
Jack Driscoll
Jack Driscoll is a central heroic character in the 1933 film "King Kong," serving as the ship's first mate and the primary human protagonist who helps rescue Ann Darrow from the giant ape.
-
E.
Matthew Sands
Matthew Sands was an American physicist and educator best known as one of the co-authors of the influential textbook series "The Feynman Lectures on Physics."
- 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 De Grasse Triple: [The Black Pirate, starring, Sam De Grasse]
Generated description
Sam De Grasse was a Canadian-born silent film actor best known for his villainous roles in early Hollywood adventure and drama films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sam De Grasse Target entity description: Sam De Grasse was a Canadian-born silent film actor best known for his villainous roles in early Hollywood adventure and drama films.
-
A.
Sam Wheeler
Sam Wheeler is the father of Ted Wheeler, the mayor of Portland, Oregon.
-
B.
Samuel Jones
Samuel Jones is an individual whose full given name is Samuel but is commonly referred to as Sam Jones.
-
C.
William Ryan
William Ryan is a marine geologist and oceanographer known for his influential work on seafloor spreading and the geological history of the Black Sea, including the "Noah's Flood" hypothesis.
-
D.
Jack Driscoll
Jack Driscoll is a central heroic character in the 1933 film "King Kong," serving as the ship's first mate and the primary human protagonist who helps rescue Ann Darrow from the giant ape.
-
E.
Matthew Sands
Matthew Sands was an American physicist and educator best known as one of the co-authors of the influential textbook series "The Feynman Lectures on Physics."
- 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_69a2e7e4676c81909ea0dbdecac0687c |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ef691cc8819091729eaac52c9457 |
completed | Feb. 28, 2026, 1:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4c66b91608190aff4623917cf3ae2 |
completed | March 1, 2026, 11:06 p.m. |
| NEDg | Description generation | batch_69a4c735b70c8190b281c88bc8a4f888 |
completed | March 1, 2026, 11:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a4c7a59c4881908d13d0f7eb0ba334 |
completed | March 1, 2026, 11:11 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.