Triple
T12255296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Henry de Young |
E292083
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object |
Phyllis de Young
Phyllis de Young was a member of the prominent de Young family associated with San Francisco journalism and philanthropy.
|
E1005460
|
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: Phyllis de Young | Statement: [Michael Henry de Young, hasChild, Phyllis de Young]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Phyllis de Young Context triple: [Michael Henry de Young, hasChild, Phyllis de Young]
-
A.
Phyllis Garr
Phyllis Garr is the mother of American actress and comedian Teri Garr.
-
B.
Phyllis Ralph
Phyllis Ralph was the wife of English stage and film actor Lionel Atwill, known for his prominent roles in early 20th-century horror and mystery films.
-
C.
Phyllis Kirk
Phyllis Kirk was an American actress best known for her roles in 1950s film noir and horror, including the classic 3D film "House of Wax."
-
D.
Phyllis Carlyle
Phyllis Carlyle was a film producer best known for her work on influential 1990s movies, including the psychological thriller "Seven."
-
E.
Phyllis Logan
Phyllis Logan is a Scottish actress best known for her award-winning film and television roles, including her portrayal of housekeeper Mrs. Hughes in the series "Downton Abbey."
- 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: Phyllis de Young Triple: [Michael Henry de Young, hasChild, Phyllis de Young]
Generated description
Phyllis de Young was a member of the prominent de Young family associated with San Francisco journalism and philanthropy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Phyllis de Young Target entity description: Phyllis de Young was a member of the prominent de Young family associated with San Francisco journalism and philanthropy.
-
A.
Phyllis Garr
Phyllis Garr is the mother of American actress and comedian Teri Garr.
-
B.
Phyllis Ralph
Phyllis Ralph was the wife of English stage and film actor Lionel Atwill, known for his prominent roles in early 20th-century horror and mystery films.
-
C.
Phyllis Kirk
Phyllis Kirk was an American actress best known for her roles in 1950s film noir and horror, including the classic 3D film "House of Wax."
-
D.
Phyllis Carlyle
Phyllis Carlyle was a film producer best known for her work on influential 1990s movies, including the psychological thriller "Seven."
-
E.
Phyllis Logan
Phyllis Logan is a Scottish actress best known for her award-winning film and television roles, including her portrayal of housekeeper Mrs. Hughes in the series "Downton Abbey."
- 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_69d6ab67950c8190be08450a06228c4b |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cc9dd5081908880061d52351850 |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f68e9fc934819089f68bcc823015da |
completed | May 2, 2026, 11:54 p.m. |
| NEDg | Description generation | batch_69f69277d3d88190abce800c01a8026c |
completed | May 3, 2026, 12:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6938150608190a61206cefa3eca1e |
completed | May 3, 2026, 12:14 a.m. |
Created at: April 8, 2026, 9:52 p.m.