Triple
T18335307
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joe Weider |
E439255
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Betty Weider
Betty Weider is an American fitness advocate, former model, and magazine writer known for promoting women's health and co-founding Shape magazine.
|
E1319030
|
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: Betty Weider | Statement: [Joe Weider, spouse, Betty Weider]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Betty Weider Context triple: [Joe Weider, spouse, Betty Weider]
-
A.
John Peterman
John Peterman is an American entrepreneur best known as the charismatic founder of the J. Peterman Company, a catalog retailer famed for its literary, adventure-themed product descriptions.
-
B.
Betty Reinhardt
Betty Reinhardt was a screenwriter best known for her work on the classic 1944 film noir "Laura."
-
C.
Donna Weiss
Donna Weiss is an American songwriter best known for co-writing the hit song "Bette Davis Eyes."
-
D.
Betty Haas
Betty Haas is known as the former wife of American politician and longtime U.S. Senator Joe Lieberman.
-
E.
Betty Kaplan
Betty Kaplan is a film director and screenwriter best known for adapting literary works, including Isabel Allende’s novel "Of Love and Shadows," for the screen.
- 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: Betty Weider Triple: [Joe Weider, spouse, Betty Weider]
Generated description
Betty Weider is an American fitness advocate, former model, and magazine writer known for promoting women's health and co-founding Shape magazine.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Betty Weider Target entity description: Betty Weider is an American fitness advocate, former model, and magazine writer known for promoting women's health and co-founding Shape magazine.
-
A.
John Peterman
John Peterman is an American entrepreneur best known as the charismatic founder of the J. Peterman Company, a catalog retailer famed for its literary, adventure-themed product descriptions.
-
B.
Betty Reinhardt
Betty Reinhardt was a screenwriter best known for her work on the classic 1944 film noir "Laura."
-
C.
Donna Weiss
Donna Weiss is an American songwriter best known for co-writing the hit song "Bette Davis Eyes."
-
D.
Betty Haas
Betty Haas is known as the former wife of American politician and longtime U.S. Senator Joe Lieberman.
-
E.
Betty Kaplan
Betty Kaplan is a film director and screenwriter best known for adapting literary works, including Isabel Allende’s novel "Of Love and Shadows," for the screen.
- 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_69d8b9175fec8190af865699b4e64d8c |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e50ecc91148190aa820fcd466009ce |
completed | April 19, 2026, 5:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03c4d2db288190b5832978f53265ca |
completed | May 13, 2026, 12:24 a.m. |
| NEDg | Description generation | batch_6a03c8b9d06c8190af9e9121246b7e6f |
completed | May 13, 2026, 12:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03c989741c81908f2a50b571a8c475 |
completed | May 13, 2026, 12:44 a.m. |
Created at: April 10, 2026, 10:36 a.m.