Triple
T19312248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juan Rico |
E482999
|
entity |
| Predicate | differenceFromFilmVersion |
P125243
|
FINISHED |
| Object | book version is Filipino from Buenos Aires |
—
|
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: book version is Filipino from Buenos Aires | Statement: [Juan Rico, differenceFromFilmVersion, book version is Filipino from Buenos Aires]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: differenceFromFilmVersion Context triple: [Juan Rico, differenceFromFilmVersion, book version is Filipino from Buenos Aires]
-
A.
notableFilmVersion
Indicates that one work is a film adaptation or version of another work that is considered particularly notable or significant.
-
B.
filmAdaptationChanges
chosen
Indicates that changes have been made when adapting a work from one medium (such as a book or play) into a film.
-
C.
versionUsedInFilm
Indicates that a particular version or edition of a work is the one that was used in a specific film.
-
D.
notableDifferenceFromDisneyAdaptation
Indicates that something differs in a significant way from how it is portrayed in a Disney adaptation.
-
E.
hasFilmVersionStatus
Indicates whether and how a work has been adapted into a film, specifying the status of that film version.
- 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_69d8e8d04d5c8190baa816986f2b1d1e |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e604ce5de081909811c49f56ba94bb |
completed | April 20, 2026, 10:49 a.m. |
| PD | Predicate disambiguation | batch_69e4dd0ef66881909d489d634eee817a |
completed | April 19, 2026, 1:47 p.m. |
Created at: April 10, 2026, 1:32 p.m.