Triple
T19333844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How to Sleep |
E483566
|
entity |
| Predicate | hasFilmLengthCategory |
P74363
|
FINISHED |
| Object | one-reel |
—
|
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: one-reel | Statement: [How to Sleep, hasFilmLengthCategory, one-reel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmLengthCategory Context triple: [How to Sleep, hasFilmLengthCategory, one-reel]
-
A.
hasRunningTimeCategory
chosen
Indicates that an entity is associated with a specific category based on its running time or duration.
-
B.
filmLength
Indicates the duration or running time of a film, typically measured in units such as minutes.
-
C.
featureLengthFilm
Indicates that the subject is a film whose running time meets or exceeds the standard length considered to be a feature film.
-
D.
filmRuntimeApprox
Indicates an approximate or estimated duration of a film, rather than its exact runtime.
-
E.
hasCinematicShort
Indicates that an entity is associated with or includes a cinematic short film or short-form cinematic content.
- 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_69d8e8d13e3c81909d91d1d5ec37c095 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e61643f7b8819088a716e54a579afa |
completed | April 20, 2026, 12:04 p.m. |
| PD | Predicate disambiguation | batch_69e4dd12303c8190a2027c062b2dff40 |
completed | April 19, 2026, 1:48 p.m. |
Created at: April 10, 2026, 1:33 p.m.