Triple
T9931665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MIME |
E192660
|
entity |
| Predicate | encodingExample |
P83558
|
FINISHED |
| Object | base64 |
—
|
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 | Statement: [MIME, encodingExample, base64]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: encodingExample Context triple: [MIME, encodingExample, base64]
-
A.
encodes
Indicates that one entity contains or represents the information, instructions, or structure of another in a coded or symbolic form.
-
B.
serializationExample
chosen
Indicates that one entity serves as an example or illustration of how another entity is serialized or converted into a serial form.
-
C.
encodingLibrary
Indicates that one entity is the software library or tool used to encode, transform, or serialize the other entity’s data or content.
-
D.
codeExample
Indicates that one entity provides a snippet or sample of source code that illustrates how to use, implement, or demonstrate another entity.
-
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_69ca82dd978c8190947124ab0d3315ac |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb5b54f348190b8e70e7beff6098a |
completed | April 2, 2026, 12:17 a.m. |
| PD | Predicate disambiguation | batch_69cd1d90b8a8819081748f129c0c6ab6 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:43 p.m.