Triple
T22002559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Halloweentown High |
E543366
|
entity |
| Predicate | mainCastMember |
P5563
|
FINISHED |
| Object | Emily Roeske |
—
|
NE NERFINISHED |
How this triple was built (2 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: [Halloweentown High, mainCastMember, Emily Roeske]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emily Roeske Context triple: [Halloweentown High, mainCastMember, Emily Roeske]
-
A.
Emily Roeske
chosen
Emily Roeske is an American former child actress best known for playing Sophie Piper in Disney Channel’s Halloweentown film series.
-
B.
Michelle Rausch
Michelle Rausch is a fictional character from the romantic drama film "Two Lovers."
-
C.
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."
-
D.
Lisa Gottsegen
Lisa Gottsegen is an American businesswoman and philanthropist best known as the longtime wife of actor Dustin Hoffman.
-
E.
Rebecca Kleefisch
Rebecca Kleefisch is an American Republican politician who served as Wisconsin’s lieutenant governor from 2011 to 2019.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e2c814c8190837d072789000486 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1276bf2a48190910d9c27f1c5e74f |
completed | April 28, 2026, 9:32 p.m. |
Created at: April 16, 2026, 8:20 p.m.