Triple
T3056248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beaches |
E60486
|
entity |
| Predicate | screenplayAdaptationType |
P30807
|
FINISHED |
| Object | novel adaptation |
—
|
LITERAL FINISHED |
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: novel adaptation | Statement: [Beaches, screenplayAdaptationType, novel adaptation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: screenplayAdaptationType Context triple: [Beaches, screenplayAdaptationType, novel adaptation]
-
A.
screenplayType
chosen
Indicates the specific category or format of a screenplay associated with a work or production.
-
B.
screenWriterAdaptationBy
Indicates that a person served as the screenwriter responsible for adapting an existing work into a screenplay.
-
C.
screenplayWrittenFor
Indicates that a screenplay was written specifically for a particular film, show, or production.
-
D.
screenplayBy
Indicates that a film, television show, or similar work was written or scripted by a particular person or group.
-
E.
screenplayLanguage
Indicates the language in which a screenplay is written or primarily expressed.
- F. None of above.
Provenance (3 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_69ad8578137c81908259dcb27c7d6d7c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ad9bf7ebd48190ad5748a18fa9a56a |
completed | March 8, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69ad962326e081909d5521c3d3ea3158 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3:02 p.m.