Triple
T5252908
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eva Cassidy |
E118629
|
entity |
| Predicate | father |
P120
|
FINISHED |
| Object |
Hugh Cassidy
Hugh Cassidy is the father of acclaimed American singer Eva Cassidy and was a significant influence and supporter in her musical development.
|
E525400
|
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: Hugh Cassidy | Statement: [Eva Cassidy, father, Hugh Cassidy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hugh Cassidy Context triple: [Eva Cassidy, father, Hugh Cassidy]
-
A.
Michael McCusker
Michael McCusker is an American film editor known for his work on major Hollywood productions, including the thriller "The Girl on the Train" (2016).
-
B.
Justin E. Driscoll
Justin E. Driscoll is an American energy executive who leads the New York Power Authority, the nation’s largest state-owned electric utility.
-
C.
Chris Ridenhour
Chris Ridenhour is a film composer known for scoring numerous low-budget genre movies, including works produced by The Asylum.
-
D.
Ed Scott
Ed Scott is a technology entrepreneur best known as a co-founder of BEA Systems, a major enterprise software company later acquired by Oracle.
-
E.
Phil Burke
Phil Burke is a Canadian actor best known for his role as Mickey McGinnes on the television drama series "Hell on Wheels."
- 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: Hugh Cassidy Triple: [Eva Cassidy, father, Hugh Cassidy]
Generated description
Hugh Cassidy is the father of acclaimed American singer Eva Cassidy and was a significant influence and supporter in her musical development.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hugh Cassidy Target entity description: Hugh Cassidy is the father of acclaimed American singer Eva Cassidy and was a significant influence and supporter in her musical development.
-
A.
Michael McCusker
Michael McCusker is an American film editor known for his work on major Hollywood productions, including the thriller "The Girl on the Train" (2016).
-
B.
Justin E. Driscoll
Justin E. Driscoll is an American energy executive who leads the New York Power Authority, the nation’s largest state-owned electric utility.
-
C.
Chris Ridenhour
Chris Ridenhour is a film composer known for scoring numerous low-budget genre movies, including works produced by The Asylum.
-
D.
Ed Scott
Ed Scott is a technology entrepreneur best known as a co-founder of BEA Systems, a major enterprise software company later acquired by Oracle.
-
E.
Phil Burke
Phil Burke is a Canadian actor best known for his role as Mickey McGinnes on the television drama series "Hell on Wheels."
- 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_69bd446978108190bb5f9c5c23d93f88 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7b7cd7f4819098e591df07564a52 |
completed | March 20, 2026, 4:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf8bc2fa4c8190b2c62f30ba46a8b9 |
completed | March 22, 2026, 6:27 a.m. |
| NEDg | Description generation | batch_69bf8c73d22c81908354318b80b6706e |
completed | March 22, 2026, 6:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf8ce3b4bc81908578a81de74b2245 |
completed | March 22, 2026, 6:32 a.m. |
Created at: March 20, 2026, 1:50 p.m.