Triple
T29203631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Kuhn |
E740348
|
entity |
| Predicate | notableFilmType |
P46117
|
FINISHED |
| Object | feature film |
—
|
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: feature film | Statement: [Michael Kuhn, notableFilmType, feature film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableFilmType Context triple: [Michael Kuhn, notableFilmType, feature film]
-
A.
notableProductionType
chosen
Indicates that the subject is particularly known for producing or creating instances of the specified type.
-
B.
notableProgramType
Indicates that the subject is recognized for or associated with a particular type or category of program.
-
C.
notableOriginalFilm
Indicates that a work is the original film from which another work (such as a remake, adaptation, or related production) is derived or notably based.
-
D.
notableGenreAsFilmmaker
Indicates the film genre for which a filmmaker is particularly recognized or distinguished.
-
E.
notableWinningFilm
Indicates that a film has achieved notable recognition by winning a significant award or competition.
- 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_69f07cb974108190b7e86ca489a6ebb6 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_6a03173dd4988190a38175b63cba9f09 |
completed | May 12, 2026, 12:04 p.m. |
| PD | Predicate disambiguation | batch_6a031700ade88190afd217d5966b03b4 |
completed | May 12, 2026, 12:03 p.m. |
Created at: April 28, 2026, 12:08 p.m.