Triple
T10253984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kevin Barrett |
E240414
|
entity |
| Predicate | isSpouseOf |
P13
|
FINISHED |
| Object |
Robyn Barrett
Robyn Barrett is the spouse of Kevin Barrett, an American scholar and controversial critic of U.S. foreign policy and the official 9/11 narrative.
|
E850874
|
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: Robyn Barrett | Statement: [Kevin Barrett, isSpouseOf, Robyn Barrett]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Robyn Barrett Context triple: [Kevin Barrett, isSpouseOf, Robyn Barrett]
-
A.
Mona Barrie
Mona Barrie was an English-born actress who became known in American films and on Broadway during the 1930s and 1940s.
-
B.
Ann Rork
Ann Rork was an American silent film actress and socialite best known for her roles in 1920s cinema and her later marriage to oil tycoon J. Paul Getty.
-
C.
Nita Talbot
Nita Talbot is an American actress known for her sharp-witted supporting roles in film and television, including a notable Emmy-nominated performance on the sitcom "Hogan's Heroes."
-
D.
Sarah Etheridge
Sarah Etheridge was the wife of prominent American financier and U.S. Secretary of the Treasury Lyman J. Gage.
-
E.
Karen Morley
Karen Morley was an American film actress of the 1930s, best known for her roles in pre-Code Hollywood crime dramas and social-themed films.
- 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: Robyn Barrett Triple: [Kevin Barrett, isSpouseOf, Robyn Barrett]
Generated description
Robyn Barrett is the spouse of Kevin Barrett, an American scholar and controversial critic of U.S. foreign policy and the official 9/11 narrative.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Robyn Barrett Target entity description: Robyn Barrett is the spouse of Kevin Barrett, an American scholar and controversial critic of U.S. foreign policy and the official 9/11 narrative.
-
A.
Mona Barrie
Mona Barrie was an English-born actress who became known in American films and on Broadway during the 1930s and 1940s.
-
B.
Ann Rork
Ann Rork was an American silent film actress and socialite best known for her roles in 1920s cinema and her later marriage to oil tycoon J. Paul Getty.
-
C.
Nita Talbot
Nita Talbot is an American actress known for her sharp-witted supporting roles in film and television, including a notable Emmy-nominated performance on the sitcom "Hogan's Heroes."
-
D.
Sarah Etheridge
Sarah Etheridge was the wife of prominent American financier and U.S. Secretary of the Treasury Lyman J. Gage.
-
E.
Karen Morley
Karen Morley was an American film actress of the 1930s, best known for her roles in pre-Code Hollywood crime dramas and social-themed films.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d24b9a308190bba6d8e3e22e5ee0 |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f7cec62c819083e493e0fc7c65b5 |
completed | April 9, 2026, 12:50 a.m. |
| NEDg | Description generation | batch_69d6fa3149e48190825600ee28ed7231 |
completed | April 9, 2026, 1 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d6fcbee9088190869b1fcb6f909be3 |
completed | April 9, 2026, 1:11 a.m. |
Created at: April 6, 2026, 11:30 a.m.