Triple
T7024997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hold That Blonde |
E162920
|
entity |
| Predicate | hasBlackAndWhiteColor |
P3490
|
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: [Hold That Blonde, hasBlackAndWhiteColor, black-and-white]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBlackAndWhiteColor Context triple: [Hold That Blonde, hasBlackAndWhiteColor, black-and-white]
-
A.
blackAndWhite
chosen
Indicates that something is presented or exists in only black and white, without any other colors.
-
B.
workBlackAndWhite
Indicates that the work is presented in black and white rather than in color.
-
C.
hasFilmColorType
Indicates that a film is associated with a particular color process or color classification (e.g., color, black-and-white).
-
D.
hasRGB
Indicates that an entity possesses or is associated with a specific RGB (red, green, blue) color value.
-
E.
hasColorModel
Indicates that an entity uses or is associated with a particular color representation model (such as RGB, CMYK, or HSV) for defining its colors.
- 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_69c6885b26248190a857541e3d10e299 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e5ecd4488190bf19e42de55da98b |
completed | March 27, 2026, 8:17 p.m. |
| PD | Predicate disambiguation | batch_69c6e1b8118481909d76eb6616160e80 |
completed | March 27, 2026, 7:59 p.m. |
Created at: March 27, 2026, 2:35 p.m.