Triple
T10553989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | All of Us |
E249026
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object |
Betsy Borns
Betsy Borns is a television writer and producer best known for creating the series "All of Us."
|
E947726
|
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: Betsy Borns | Statement: [All of Us, creator, Betsy Borns]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Betsy Borns Context triple: [All of Us, creator, Betsy Borns]
-
A.
Betsy McCaughey
Betsy McCaughey is an American politician, writer, and former Lieutenant Governor of New York known for her conservative commentary and opposition to certain health care reforms.
-
B.
Mary Beth Johnson
Mary Beth Johnson is known as the wife of American Western film actor Charles Starrett.
-
C.
Mary Beth Hughes
Mary Beth Hughes was an American film and television actress best known for her roles in 1940s Hollywood dramas and crime films.
-
D.
Kathleen Beavier
Kathleen Beavier is the central protagonist of James Patterson’s thriller novel "Cradle and All," around whom the book’s mysterious and suspenseful events revolve.
-
E.
Ann Schmeltz Bowers
Ann Schmeltz Bowers is an American technology executive and philanthropist known for her early leadership roles at Intel and Apple and for her significant charitable contributions, particularly in education and technology.
- 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: Betsy Borns Triple: [All of Us, creator, Betsy Borns]
Generated description
Betsy Borns is a television writer and producer best known for creating the series "All of Us."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Betsy Borns Target entity description: Betsy Borns is a television writer and producer best known for creating the series "All of Us."
-
A.
Betsy McCaughey
Betsy McCaughey is an American politician, writer, and former Lieutenant Governor of New York known for her conservative commentary and opposition to certain health care reforms.
-
B.
Mary Beth Johnson
Mary Beth Johnson is known as the wife of American Western film actor Charles Starrett.
-
C.
Mary Beth Hughes
Mary Beth Hughes was an American film and television actress best known for her roles in 1940s Hollywood dramas and crime films.
-
D.
Kathleen Beavier
Kathleen Beavier is the central protagonist of James Patterson’s thriller novel "Cradle and All," around whom the book’s mysterious and suspenseful events revolve.
-
E.
Ann Schmeltz Bowers
Ann Schmeltz Bowers is an American technology executive and philanthropist known for her early leadership roles at Intel and Apple and for her significant charitable contributions, particularly in education and technology.
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d527118da081909ca61bc555a17609 |
completed | April 7, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f12f646ec88190ab4745c52798b599 |
completed | April 28, 2026, 10:06 p.m. |
| NEDg | Description generation | batch_69f14e879aa88190a95f13e23dd346f4 |
completed | April 29, 2026, 12:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f156fa5cc48190a43c1d2e5df346fe |
completed | April 29, 2026, 12:55 a.m. |
Created at: April 6, 2026, 12:34 p.m.