Triple
T30497989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snowboy |
E776056
|
entity |
| Predicate | filmWorkType |
P136433
|
FINISHED |
| Object | musical film character |
—
|
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: musical film character | Statement: [Snowboy, filmWorkType, musical film character]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmWorkType Context triple: [Snowboy, filmWorkType, musical film character]
-
A.
filmType
Indicates the specific category or genre that a film belongs to.
-
B.
workFilmType
chosen
Indicates the type or category of film associated with a particular work.
-
C.
filmProductionType
Indicates the specific kind or category of production under which a film was made (e.g., feature, short, documentary, TV movie).
-
D.
filmGenreOfRelatedWork
Indicates that a work is related to another work through sharing or being associated with the same film genre.
-
E.
filmTypeContext
Indicates the contextual relationship between a film and its type or category within a specific classification or usage setting.
- 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_69f22498c5d481908aaea89e6fab8280 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6877e57108190802c2adbbaf16d3c |
completed | May 2, 2026, 11:23 p.m. |
| PD | Predicate disambiguation | batch_69f67e42d6688190b60e91d2c388c555 |
completed | May 2, 2026, 10:44 p.m. |
Created at: April 29, 2026, 8:14 p.m.