Triple
T29503785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Basic |
E748454
|
entity |
| Predicate | credentialEncoding |
P33682
|
FINISHED |
| Object | Base64-encoded UTF-8 string |
—
|
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: Base64-encoded UTF-8 string | Statement: [Basic, credentialEncoding, Base64-encoded UTF-8 string]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: credentialEncoding Context triple: [Basic, credentialEncoding, Base64-encoded UTF-8 string]
-
A.
credentialType
Indicates the specific kind or category of credential associated with an entity or relationship.
-
B.
addressEncoding
Indicates how an address is represented or formatted into a specific encoded form.
-
C.
encodes
Indicates that one entity contains or represents the information, instructions, or structure of another in a coded or symbolic form.
-
D.
dataEncodingMethod
chosen
Indicates the specific technique or format used to encode data for storage, transmission, or processing.
-
E.
encodingPurpose
Indicates the reason or intended use for which an encoding is created or applied.
- 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_69f0bd455a9c8190b40a3e8ea38cf61f |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69f66c34a370819089a89f26857d3dfa |
completed | May 2, 2026, 9:27 p.m. |
| PD | Predicate disambiguation | batch_69f6633ac8a88190ab0cda62bbfcf9b0 |
completed | May 2, 2026, 8:48 p.m. |
Created at: April 28, 2026, 4:26 p.m.