Triple
T14785647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Argentine cinema |
E347514
|
entity |
| Predicate | commonAdaptationSource |
P66478
|
FINISHED |
| Object | Argentine novels |
—
|
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: Argentine novels | Statement: [Argentine cinema, commonAdaptationSource, Argentine novels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonAdaptationSource Context triple: [Argentine cinema, commonAdaptationSource, Argentine novels]
-
A.
adaptsFrom
chosen
Indicates that one entity is derived, modified, or transformed from another, preserving core elements while changing form, medium, or context.
-
B.
usedInAdaptation
Indicates that something (such as a character, plot element, or work) is incorporated or appears within an adaptation of another original source.
-
C.
adaptationType
Indicates the specific kind or category of adaptation that relates one entity to another or to a particular context.
-
D.
isAdaptation
Indicates that one work is derived from, based on, or reinterprets the content of another work.
-
E.
mediaAdaptationSettingOf
Indicates that a media adaptation (such as a film, series, or game) is set in the narrative world or environment of a particular original work.
- 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_69d822e9b9e08190bedcc31a163fda82 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decaa083e481908336d58d026eec32 |
completed | April 14, 2026, 11:15 p.m. |
| PD | Predicate disambiguation | batch_69de8c090d1081909b5a9bf437499d6c |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:31 a.m.