Triple
T35432649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Arrangement |
E1024104
|
entity |
| Predicate | characterPlayedByAutumnReeser |
P200627
|
FINISHED |
| Object | Leslie Bellcamp |
—
|
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: Leslie Bellcamp | Statement: [The Arrangement, characterPlayedByAutumnReeser, Leslie Bellcamp]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterPlayedByAutumnReeser Context triple: [The Arrangement, characterPlayedByAutumnReeser, Leslie Bellcamp]
-
A.
characterPlayedBy Larisa Oleynik
Indicates that a specific fictional character is portrayed or acted by Larisa Oleynik.
-
B.
characterPlayedBy Emmanuelle Chriqui
Indicates that the role or character in question is portrayed or acted by Emmanuelle Chriqui.
-
C.
characterPlayedByAlexandraShipp
Indicates that a given character is portrayed or acted by Alexandra Shipp.
-
D.
characterPortrayedByCheyenneJackson
Indicates that a given character is portrayed or played by the actor Cheyenne Jackson.
-
E.
characterPlayedBy_MichelleRyan
Indicates that the subject is a character portrayed or played by Michelle Ryan.
- F. None of above. chosen
Provenance (4 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_69f76df743c48190aecb6dd79efb0d95 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff9bed58dc8190a204816d4ed6c32c |
completed | May 9, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69ff9b69653c81908ab0d88055a66a88 |
completed | May 9, 2026, 8:39 p.m. |
| PDg | Predicate description generation | batch_69ff9bec9d748190869caebf6bf0c78f |
completed | May 9, 2026, 8:41 p.m. |
Created at: May 3, 2026, 4:03 p.m.