Triple
T1096579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | sRGB |
E24283
|
entity |
| Predicate | hasDecoding |
P14388
|
FINISHED |
| Object | linear light values for internal colorimetric calculations |
—
|
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: linear light values for internal colorimetric calculations | Statement: [sRGB, hasDecoding, linear light values for internal colorimetric calculations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDecoding Context triple: [sRGB, hasDecoding, linear light values for internal colorimetric calculations]
-
A.
decodingMethod
chosen
Indicates the technique or process used to convert encoded or encrypted data back into its original, interpretable form.
-
B.
hasDigitalEncoding
Indicates that one entity is represented, stored, or expressed using a specific digital code or encoding scheme provided by another entity.
-
C.
deciphermentStatus
Indicates the degree to which something (such as a text, code, or inscription) has been successfully decoded or interpreted.
-
D.
hasUnicode
Indicates that an entity is associated with, represented by, or encoded using a specific Unicode character or sequence.
-
E.
encodedIn
Indicates that one entity is represented, stored, or expressed within another entity using a specific encoding or format.
- 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_69a4940542308190ac2a0b1f730b7cfc |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b99ffb3481908cd168b6c58e1c6d |
completed | March 1, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69a4b7448c148190a3c9a4158ebd05b4 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.