Triple
T23264151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Justin Fonda |
E582098
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object |
Mary Marjorie Ford
Mary Marjorie Ford is a member of the Fonda family, known primarily for her familial connection to actor Justin Fonda.
|
E1576863
|
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: Mary Marjorie Ford | Statement: [Justin Fonda, relative, Mary Marjorie Ford]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Marjorie Ford Context triple: [Justin Fonda, relative, Mary Marjorie Ford]
-
A.
Margaret Ford
Margaret Ford is a successful psychiatrist who becomes entangled in the dangerous world of con artists in David Mamet’s psychological thriller film "House of Games."
-
B.
Elsie Ann Ford
Elsie Ann Ford was an American actress and the mother of actor Robert Downey Jr.
-
C.
Louise Ford
Louise Ford is a film editor known for her work on acclaimed movies such as the psychological horror film "The Lighthouse."
-
D.
Louise Ford
Louise Ford is a British actress and comedian known for her work in television sketch shows and sitcoms, as well as for her relationship with actor Rowan Atkinson.
-
E.
Eleanor Clay Ford
Eleanor Clay Ford was an American heiress and prominent Detroit philanthropist, best known as the wife of Edsel Ford and a key figure in the Ford family legacy.
- 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: Mary Marjorie Ford Triple: [Justin Fonda, relative, Mary Marjorie Ford]
Generated description
Mary Marjorie Ford is a member of the Fonda family, known primarily for her familial connection to actor Justin Fonda.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mary Marjorie Ford Target entity description: Mary Marjorie Ford is a member of the Fonda family, known primarily for her familial connection to actor Justin Fonda.
-
A.
Margaret Ford
Margaret Ford is a successful psychiatrist who becomes entangled in the dangerous world of con artists in David Mamet’s psychological thriller film "House of Games."
-
B.
Elsie Ann Ford
Elsie Ann Ford was an American actress and the mother of actor Robert Downey Jr.
-
C.
Louise Ford
Louise Ford is a British actress and comedian known for her work in television sketch shows and sitcoms, as well as for her relationship with actor Rowan Atkinson.
-
D.
Louise Ford
Louise Ford is a film editor known for her work on acclaimed movies such as the psychological horror film "The Lighthouse."
-
E.
Eleanor Clay Ford
Eleanor Clay Ford was an American heiress and prominent Detroit philanthropist, best known as the wife of Edsel Ford and a key figure in the Ford family legacy.
- 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_69e246079f58819085eaa9c260906880 |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f194cb76c48190869915cd93b44fcc |
completed | April 29, 2026, 5:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c3f723d4081909c2ff75ba4ada497 |
completed | May 19, 2026, 10:46 a.m. |
| NEDg | Description generation | batch_6a0c405be1188190b6622665e448bbe7 |
completed | May 19, 2026, 10:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c40c6562481908689eca2a4997115 |
completed | May 19, 2026, 10:51 a.m. |
Created at: April 17, 2026, 4:11 p.m.