Triple
T6975317
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leahy |
E161701
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Mary Leahy
Mary Leahy is a notable individual who shares the surname Leahy and has achieved sufficient recognition to be specifically cited as a bearer of the name.
|
E793971
|
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 Leahy | Statement: [Leahy, hasNotableBearer, Mary Leahy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Leahy Context triple: [Leahy, hasNotableBearer, Mary Leahy]
-
A.
Mary Mulhern
Mary Mulhern was an American actress best known for her brief Hollywood career in the silent film era and her marriage to actor Jack Pickford.
-
B.
Mary Healy
Mary Healy was an American actress and singer known for her work in film, television, and on stage, often performing alongside her husband, entertainer Peter Lind Hayes.
-
C.
Mary Connelly
Mary Connelly is a television producer best known for her work as an executive producer on major daytime talk shows, including The Jennifer Hudson Show.
-
D.
Mary McCleary
Mary McCleary is best known as the wife of famed American automobile executive Lee Iacocca.
-
E.
Margaret O'Leary
Margaret O'Leary is an Irish-born American nurse and educator known for her contributions to nursing practice and education in the United States.
- 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 Leahy Triple: [Leahy, hasNotableBearer, Mary Leahy]
Generated description
Mary Leahy is a notable individual who shares the surname Leahy and has achieved sufficient recognition to be specifically cited as a bearer of the name.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mary Leahy Target entity description: Mary Leahy is a notable individual who shares the surname Leahy and has achieved sufficient recognition to be specifically cited as a bearer of the name.
-
A.
Mary Mulhern
Mary Mulhern was an American actress best known for her brief Hollywood career in the silent film era and her marriage to actor Jack Pickford.
-
B.
Mary Healy
Mary Healy was an American actress and singer known for her work in film, television, and on stage, often performing alongside her husband, entertainer Peter Lind Hayes.
-
C.
Mary Connelly
Mary Connelly is a television producer best known for her work as an executive producer on major daytime talk shows, including The Jennifer Hudson Show.
-
D.
Mary McCleary
Mary McCleary is best known as the wife of famed American automobile executive Lee Iacocca.
-
E.
Margaret O'Leary
Margaret O'Leary is an Irish-born American nurse and educator known for her contributions to nursing practice and education in the United States.
- 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_69c68854a0d88190bc0bf82263f1afce |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db3d3ab08190b107f3229c357dd2 |
completed | March 27, 2026, 7:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0f32101e08190888a1f44c2224e1e |
completed | April 4, 2026, 11:16 a.m. |
| NEDg | Description generation | batch_69d0f46bd034819093e7157a3e1ac1fc |
completed | April 4, 2026, 11:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d0f5bf64548190b40e97b279db5105 |
completed | April 4, 2026, 11:27 a.m. |
Created at: March 27, 2026, 2:31 p.m.