Triple
T23811793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dirty Computer |
E589876
|
entity |
| Predicate | hasMusicVideoCompilation |
P85640
|
FINISHED |
| Object | Dirty Computer: An Emotion Picture |
—
|
NE NERFINISHED |
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: Dirty Computer: An Emotion Picture | Statement: [Dirty Computer, hasMusicVideoCompilation, Dirty Computer: An Emotion Picture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMusicVideoCompilation Context triple: [Dirty Computer, hasMusicVideoCompilation, Dirty Computer: An Emotion Picture]
-
A.
hasMusicVideo
Indicates that a piece of media (typically a song) is associated with or accompanied by a music video.
-
B.
hasFilmCompilation
chosen
Indicates a relationship where one entity is a compilation that includes or aggregates multiple films as its components.
-
C.
hasMusicVideoCharacteristic
Indicates that a music video possesses a specific attribute, feature, or quality.
-
D.
hasMusicVideoForEverySong
Indicates that for each song in a given collection or repertoire, there exists a corresponding music video.
-
E.
hasNumberOfCompilationAlbums
Indicates the total count of compilation albums associated with a given entity.
- 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_69e25d19fecc8190a5cf39bbb18d5d7f |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1c75715d48190ac55713de9d5ba4f |
completed | April 29, 2026, 8:54 a.m. |
| PD | Predicate disambiguation | batch_69f155fe300481909bd617443228df65 |
completed | April 29, 2026, 12:51 a.m. |
Created at: April 17, 2026, 7:57 p.m.