Triple
T5508614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darnell Nurse |
E144505
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object |
Sarah Nurse
Sarah Nurse is a Canadian professional ice hockey forward known for starring with the national women’s team and winning Olympic gold.
|
E531464
|
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: Sarah Nurse | Statement: [Darnell Nurse, relative, Sarah Nurse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sarah Nurse Context triple: [Darnell Nurse, relative, Sarah Nurse]
-
A.
Elizabeth Armistead
Elizabeth Armistead was an 18th-century English courtesan and later the respected wife and companion of prominent Whig statesman Charles James Fox.
-
B.
Cathleen Summers
Cathleen Summers is a film and television producer best known for her work on the action-comedy movie "Stakeout."
-
C.
Sarah Frye
Sarah Frye is a notable individual distinguished enough to be recognized as a prominent bearer of the Frye surname.
-
D.
Lucy McFadden
Lucy McFadden is the young daughter of the main female protagonist in the romantic comedy film "The Goodbye Girl," whose presence and personality significantly shape the story’s emotional core.
-
E.
Beth Nolan
Beth Nolan is an American lawyer and legal scholar who served as White House Counsel to President Bill Clinton.
- 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: Sarah Nurse Triple: [Darnell Nurse, relative, Sarah Nurse]
Generated description
Sarah Nurse is a Canadian professional ice hockey forward known for starring with the national women’s team and winning Olympic gold.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sarah Nurse Target entity description: Sarah Nurse is a Canadian professional ice hockey forward known for starring with the national women’s team and winning Olympic gold.
-
A.
Elizabeth Armistead
Elizabeth Armistead was an 18th-century English courtesan and later the respected wife and companion of prominent Whig statesman Charles James Fox.
-
B.
Cathleen Summers
Cathleen Summers is a film and television producer best known for her work on the action-comedy movie "Stakeout."
-
C.
Sarah Frye
Sarah Frye is a notable individual distinguished enough to be recognized as a prominent bearer of the Frye surname.
-
D.
Lucy McFadden
Lucy McFadden is the young daughter of the main female protagonist in the romantic comedy film "The Goodbye Girl," whose presence and personality significantly shape the story’s emotional core.
-
E.
Beth Nolan
Beth Nolan is an American lawyer and legal scholar who served as White House Counsel to President Bill Clinton.
- 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_69c008f6b5048190a09064116062cf69 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f4a80d88190bab0056c4c78be93 |
completed | March 22, 2026, 4:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c027c4b6c0819086c7c64911c7106e |
completed | March 22, 2026, 5:32 p.m. |
| NEDg | Description generation | batch_69c033db6ebc8190ab35c707b846426d |
completed | March 22, 2026, 6:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c0345cd5b88190855efd8c1bb94693 |
completed | March 22, 2026, 6:26 p.m. |
Created at: March 22, 2026, 3:33 p.m.