Triple
T22167829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fred Ott's Sneeze |
E547841
|
entity |
| Predicate | subjectOfTheFilm |
P22751
|
FINISHED |
| Object | Fred Ott sneezing after inhaling snuff |
—
|
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: Fred Ott sneezing after inhaling snuff | Statement: [Fred Ott's Sneeze, subjectOfTheFilm, Fred Ott sneezing after inhaling snuff]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectOfTheFilm Context triple: [Fred Ott's Sneeze, subjectOfTheFilm, Fred Ott sneezing after inhaling snuff]
-
A.
subjectOfFilm
chosen
Indicates that a person, character, or topic is the main focus or central topic depicted in a particular film.
-
B.
sourceFilmTitle
Indicates the title of the film from which a referenced work, element, or derivative content originates.
-
C.
filmAssociatedWith
Indicates a general relationship or connection between a film and another entity, such as a person, organization, event, or work.
-
D.
filmWithinFilmTitle
Indicates that a title refers to a fictional film that appears within another (primary) film.
-
E.
titleSubjectOf
Indicates that a title (such as a book, article, or work) is about or primarily concerns a particular subject.
- 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_69e11e3c4c5c81908d336165816b12e0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12a6642b08190980fa0c0d2bb4229 |
completed | April 28, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69e71b41555881909b8e22718974d527 |
completed | April 21, 2026, 6:37 a.m. |
Created at: April 16, 2026, 8:34 p.m.