Triple
T11430728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Be Our Guest |
E270870
|
entity |
| Predicate | hasFilmSequenceType |
P77503
|
FINISHED |
| Object | show-stopping production number |
—
|
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: show-stopping production number | Statement: [Be Our Guest, hasFilmSequenceType, show-stopping production number]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmSequenceType Context triple: [Be Our Guest, hasFilmSequenceType, show-stopping production number]
-
A.
sequenceInFilm
chosen
Indicates that one entity is a specific sequence or segment that appears within the narrative or structure of a particular film.
-
B.
hasSequelType
Indicates that one work has a sequel of a specified type or category in relation to another work.
-
C.
hasFilmStyle
Indicates that a film exhibits or is characterized by a particular cinematic style or aesthetic approach.
-
D.
hasAnimatedSequences
Indicates that the subject contains or includes one or more animated sequences.
-
E.
filmSerialType
Indicates the type or category of a film serial to which a particular film or episode belongs.
- 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_69d6aadeef688190874bcecd88b3dd9b |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d806c1bfb881909720c74fe0fa837f |
completed | April 9, 2026, 8:06 p.m. |
| PD | Predicate disambiguation | batch_69d7e71436f88190ac7e45a04ea5c987 |
completed | April 9, 2026, 5:51 p.m. |
Created at: April 8, 2026, 9:35 p.m.