Triple
T13021425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Smile Please |
E326179
|
entity |
| Predicate | performer |
P1363
|
FINISHED |
| Object |
Shirley Brewer
Shirley Brewer is a vocalist known for her performance of the song "Smile Please."
|
E1242181
|
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: Shirley Brewer | Statement: [Smile Please, performer, Shirley Brewer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shirley Brewer Context triple: [Smile Please, performer, Shirley Brewer]
-
A.
Sherry Nelson
Sherry Nelson is known as the former spouse of Academy Award–winning American actor Rod Steiger.
-
B.
Betty Flanders
Betty Flanders is a central maternal figure in Virginia Woolf’s novel "Jacob’s Room," depicted as the widowed mother of the protagonist, Jacob Flanders.
-
C.
Shirley Congdon
Shirley Congdon is a British academic leader and nurse educator who serves as the Vice-Chancellor of the University of Bradford.
-
D.
Marjorie Weaver
Marjorie Weaver was an American film actress of the 1930s and 1940s, known for her roles in Hollywood studio productions including several notable dramas and comedies.
-
E.
Brenda Hosbrook
Brenda Hosbrook is an individual whose specific public background or notable achievements are not widely documented.
- 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: Shirley Brewer Triple: [Smile Please, performer, Shirley Brewer]
Generated description
Shirley Brewer is a vocalist known for her performance of the song "Smile Please."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shirley Brewer Target entity description: Shirley Brewer is a vocalist known for her performance of the song "Smile Please."
-
A.
Sherry Nelson
Sherry Nelson is known as the former spouse of Academy Award–winning American actor Rod Steiger.
-
B.
Betty Flanders
Betty Flanders is a central maternal figure in Virginia Woolf’s novel "Jacob’s Room," depicted as the widowed mother of the protagonist, Jacob Flanders.
-
C.
Shirley Congdon
Shirley Congdon is a British academic leader and nurse educator who serves as the Vice-Chancellor of the University of Bradford.
-
D.
Marjorie Weaver
Marjorie Weaver was an American film actress of the 1930s and 1940s, known for her roles in Hollywood studio productions including several notable dramas and comedies.
-
E.
Brenda Hosbrook
Brenda Hosbrook is an individual whose specific public background or notable achievements are not widely documented.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97ecf21bc819082fb512bc479b4be |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00cfb725d48190bdca0a85ca7f440c |
completed | May 10, 2026, 6:34 p.m. |
| NEDg | Description generation | batch_6a00d1a88c188190bde3fe3c01efb453 |
completed | May 10, 2026, 6:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00d20ae52c8190b4c45af68aa40399 |
completed | May 10, 2026, 6:44 p.m. |
Created at: April 9, 2026, 8:52 p.m.