Triple
T22996567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pictures of Dust |
E572209
|
entity |
| Predicate | hasTitle |
P38
|
FINISHED |
| Object | Pictures of Dust |
—
|
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: Pictures of Dust | Statement: [Pictures of Dust, hasTitle, Pictures of Dust]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pictures of Dust Context triple: [Pictures of Dust, hasTitle, Pictures of Dust]
-
A.
Pictures of Dust
chosen
Pictures of Dust is a conceptual art series by Vik Muniz in which he recreates and photographs images using dust collected from museum floors, exploring themes of memory, absence, and materiality.
-
B.
Dust
Dust is a crime novel by Patricia Cornwell featuring medical examiner Dr. Kay Scarpetta as she investigates a complex murder case linked to powerful institutions.
-
C.
Dust
Dust is a mysterious, conscious elementary particle central to the metaphysical and theological themes of Philip Pullman’s His Dark Materials universe.
-
D.
Dust
Dust is a musical artist known for composing the score for the work "Learning To Die."
-
E.
Dust
Dust is a Marvel Comics mutant superhero, often associated with the X-Men, who can transform her body into a swirling cloud of sand-like particles.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245b535808190adef8a9df3c584db |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f182f3186c81909e0d5177029a72ae |
completed | April 29, 2026, 4:02 a.m. |
Created at: April 17, 2026, 3:50 p.m.