Triple
T16286152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Slipstream |
E395392
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Tony Kayden
Tony Kayden is a film and television screenwriter best known for his work on the science fiction movie "Slipstream."
|
E1205149
|
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: Tony Kayden | Statement: [Slipstream, screenwriter, Tony Kayden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tony Kayden Context triple: [Slipstream, screenwriter, Tony Kayden]
-
A.
Jon Callas
Jon Callas is a cryptographer and security expert known for co-founding PGP Corporation and developing secure communication technologies.
-
B.
David Tomlinson
David Tomlinson was an English actor best known for his comedic roles in classic Disney films such as "Mary Poppins" and "Bedknobs and Broomsticks."
-
C.
Jack Welker
Jack Welker is a ruthless white supremacist gang leader and major antagonist in the television series "Breaking Bad."
-
D.
Keenan Wynn
Keenan Wynn was an American character actor known for his prolific film and television career from the 1940s through the 1970s, often playing gruff, comedic, or villainous supporting roles.
-
E.
Glenn Vernon
Glenn Vernon was an American film and stage actor active primarily in the 1940s and 1950s, known for his supporting roles in Hollywood productions.
- 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: Tony Kayden Triple: [Slipstream, screenwriter, Tony Kayden]
Generated description
Tony Kayden is a film and television screenwriter best known for his work on the science fiction movie "Slipstream."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tony Kayden Target entity description: Tony Kayden is a film and television screenwriter best known for his work on the science fiction movie "Slipstream."
-
A.
Jon Callas
Jon Callas is a cryptographer and security expert known for co-founding PGP Corporation and developing secure communication technologies.
-
B.
David Tomlinson
David Tomlinson was an English actor best known for his comedic roles in classic Disney films such as "Mary Poppins" and "Bedknobs and Broomsticks."
-
C.
Jack Welker
Jack Welker is a ruthless white supremacist gang leader and major antagonist in the television series "Breaking Bad."
-
D.
Keenan Wynn
Keenan Wynn was an American character actor known for his prolific film and television career from the 1940s through the 1970s, often playing gruff, comedic, or villainous supporting roles.
-
E.
Glenn Vernon
Glenn Vernon was an American film and stage actor active primarily in the 1940s and 1950s, known for his supporting roles in Hollywood productions.
- 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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24914bda08190a5d6315414ee3f76 |
completed | April 17, 2026, 2:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001f94ede48190835e8a0c6f5d0f19 |
completed | May 10, 2026, 6:03 a.m. |
| NEDg | Description generation | batch_6a002067aa708190bc2583c95ab133a4 |
completed | May 10, 2026, 6:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00214a2a908190a11388a63de1f7af |
completed | May 10, 2026, 6:10 a.m. |
Created at: April 10, 2026, 5:05 a.m.