Triple
T26850569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Girl of the Limberlost |
E676045
|
entity |
| Predicate | filmColorFormat |
P13343
|
FINISHED |
| Object | black-and-white 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: black-and-white film | Statement: [The Girl of the Limberlost, filmColorFormat, black-and-white film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmColorFormat Context triple: [The Girl of the Limberlost, filmColorFormat, black-and-white film]
-
A.
hasFilmColorType
chosen
Indicates that a film is associated with a particular color process or color classification (e.g., color, black-and-white).
-
B.
hasColorFormat
Indicates that an entity uses or is associated with a specific color representation or encoding format.
-
C.
colorPrimaries
Indicates the primary color components or base colors used to define the color representation of an image or visual signal.
-
D.
chromaSubsamplingOptions
Indicates how color information is reduced or sampled relative to luminance in an image or video signal.
-
E.
supportsColorSampling
Indicates that one entity can perform or accommodate color sampling operations on another entity or its data.
- 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_69eee9b9d7708190a15d7485709ae981 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f61b91003881908a3cf1f494a5b046 |
completed | May 2, 2026, 3:43 p.m. |
| PD | Predicate disambiguation | batch_69f611ad2eb48190ac1ed0090f13f7a9 |
completed | May 2, 2026, 3:01 p.m. |
Created at: April 27, 2026, 5:16 a.m.