Triple
T21745759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sophie Piper |
E536782
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object |
Emily Roeske
Emily Roeske is an American former child actress best known for playing Sophie Piper in Disney Channel’s Halloweentown film series.
|
E1562843
|
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: Emily Roeske | Statement: [Sophie Piper, portrayedBy, Emily Roeske]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emily Roeske Context triple: [Sophie Piper, portrayedBy, Emily Roeske]
-
A.
Michelle Rausch
Michelle Rausch is a fictional character from the romantic drama film "Two Lovers."
-
B.
Emily Riedel
Emily Riedel is an American gold dredge captain and opera singer best known as a central cast member on the reality TV series "Bering Sea Gold."
-
C.
Lisa Gottsegen
Lisa Gottsegen is an American businesswoman and philanthropist best known as the longtime wife of actor Dustin Hoffman.
-
D.
Rebecca Kleefisch
Rebecca Kleefisch is an American Republican politician who served as Wisconsin’s lieutenant governor from 2011 to 2019.
-
E.
Rae Schollmaier
Rae Schollmaier is a benefactor and namesake of Texas Christian University's Ed and Rae Schollmaier Arena, recognized for her significant support of the institution's athletic programs.
- 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: Emily Roeske Triple: [Sophie Piper, portrayedBy, Emily Roeske]
Generated description
Emily Roeske is an American former child actress best known for playing Sophie Piper in Disney Channel’s Halloweentown film series.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Emily Roeske Target entity description: Emily Roeske is an American former child actress best known for playing Sophie Piper in Disney Channel’s Halloweentown film series.
-
A.
Michelle Rausch
Michelle Rausch is a fictional character from the romantic drama film "Two Lovers."
-
B.
Emily Riedel
Emily Riedel is an American gold dredge captain and opera singer best known as a central cast member on the reality TV series "Bering Sea Gold."
-
C.
Lisa Gottsegen
Lisa Gottsegen is an American businesswoman and philanthropist best known as the longtime wife of actor Dustin Hoffman.
-
D.
Rebecca Kleefisch
Rebecca Kleefisch is an American Republican politician who served as Wisconsin’s lieutenant governor from 2011 to 2019.
-
E.
Rae Schollmaier
Rae Schollmaier is a benefactor and namesake of Texas Christian University's Ed and Rae Schollmaier Arena, recognized for her significant support of the institution's athletic programs.
- 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_69e0c46df5448190b4322127ffc4c690 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01a76540c8190b91a67f4a70869fb |
completed | April 28, 2026, 2:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0bc9fd93e881908c7998b5a8118fad |
completed | May 19, 2026, 2:25 a.m. |
| NEDg | Description generation | batch_6a0bcb094b60819090c7550dad826fac |
completed | May 19, 2026, 2:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0bcb7ccc4881909fe70749449c0e6c |
completed | May 19, 2026, 2:31 a.m. |
Created at: April 16, 2026, 6:49 p.m.