Triple
T12533650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greer |
E299630
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Rachel Greer
Rachel Greer is a professional known for her expertise in Amazon marketplace compliance, product safety, and e-commerce consulting.
|
E1097475
|
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: Rachel Greer | Statement: [Greer, hasNotableBearer, Rachel Greer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rachel Greer Context triple: [Greer, hasNotableBearer, Rachel Greer]
-
A.
Lisa Greer
Lisa Greer is a notable individual recognized for her contributions and public profile associated with the surname Greer.
-
B.
Sarah Greer
Sarah Greer is a British academic and higher education leader who serves as Vice-Chancellor of the University of Winchester.
-
C.
Linda Greene
Linda Greene is the daughter of Canadian actor and broadcaster Lorne Greene, famed for his role in the television series "Bonanza."
-
D.
Eileen Morrow
Eileen Morrow is a person notable enough to be recognized as a significant bearer of the surname Morrow.
-
E.
Patricia Greene
Patricia Greene is a British actress best known for her long-running role as Jill Archer in the BBC radio soap opera "The Archers."
- 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: Rachel Greer Triple: [Greer, hasNotableBearer, Rachel Greer]
Generated description
Rachel Greer is a professional known for her expertise in Amazon marketplace compliance, product safety, and e-commerce consulting.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rachel Greer Target entity description: Rachel Greer is a professional known for her expertise in Amazon marketplace compliance, product safety, and e-commerce consulting.
-
A.
Lisa Greer
Lisa Greer is a notable individual recognized for her contributions and public profile associated with the surname Greer.
-
B.
Sarah Greer
Sarah Greer is a British academic and higher education leader who serves as Vice-Chancellor of the University of Winchester.
-
C.
Linda Greene
Linda Greene is the daughter of Canadian actor and broadcaster Lorne Greene, famed for his role in the television series "Bonanza."
-
D.
Eileen Morrow
Eileen Morrow is a person notable enough to be recognized as a significant bearer of the surname Morrow.
-
E.
Patricia Greene
Patricia Greene is a British actress best known for her long-running role as Jill Archer in the BBC radio soap opera "The Archers."
- 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_69d6ada5cdd48190860d9ce30aff69be |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d9546b8fd48190ae90e80785b2e2d1 |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd54f264d48190be636796d694ceb1 |
completed | May 8, 2026, 3:13 a.m. |
| NEDg | Description generation | batch_69fd570e482881909532000eebd169d1 |
completed | May 8, 2026, 3:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd57710f648190a1344ac1363acce1 |
completed | May 8, 2026, 3:24 a.m. |
Created at: April 8, 2026, 9:57 p.m.