Triple
T3174938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Ox-Bow Incident |
E66438
|
entity |
| Predicate | filmColorType |
P13343
|
FINISHED |
| Object | black-and-white |
—
|
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 | Statement: [The Ox-Bow Incident, filmColorType, black-and-white]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmColorType Context triple: [The Ox-Bow Incident, filmColorType, black-and-white]
-
A.
hasFilmColorType
chosen
Indicates that a film is associated with a particular color process or color classification (e.g., color, black-and-white).
-
B.
supportsColorSampling
Indicates that one entity can perform or accommodate color sampling operations on another entity or its data.
-
C.
filmType
Indicates the specific category or genre that a film belongs to.
-
D.
supportsWideColorGamut
Indicates that one entity provides or enables compatibility with a wide color gamut capability for another entity or context.
-
E.
colors
Indicates that one entity assigns, describes, or provides the color or colors of another 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_69ad8586a34c8190944c63ec11a8de1a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada671e6848190a683eec1519b9268 |
completed | March 8, 2026, 4:40 p.m. |
| PD | Predicate disambiguation | batch_69ad9e02677c8190a21d93b1259b2761 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:06 p.m.