Triple
T33433657
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TextEncoderStream |
E856193
|
entity |
| Predicate | encodesTo |
P1444
|
FINISHED |
| Object | UTF-8 |
—
|
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: UTF-8 | Statement: [TextEncoderStream, encodesTo, UTF-8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: encodesTo Context triple: [TextEncoderStream, encodesTo, UTF-8]
-
A.
encodes
Indicates that one entity contains or represents the information, instructions, or structure of another in a coded or symbolic form.
-
B.
encodedIn
chosen
Indicates that one entity is represented, stored, or expressed within another entity using a specific encoding or format.
-
C.
encodingAbstraction
Indicates an abstraction relationship where one representation or model encodes, summarizes, or symbolically captures the structure or information content of another.
-
D.
encodesDirectly
Indicates that one entity contains and specifies the exact information needed to represent another entity without intermediate transformation or interpretation.
-
E.
encodingBasisFor
Indicates that one encoding scheme serves as the foundational or reference basis for defining or interpreting another encoding.
- 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_69f349709e7881908c342b4d34f555f4 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6e4832c6c8190b0575df4ee79df5d |
completed | May 3, 2026, 6 a.m. |
| PD | Predicate disambiguation | batch_69f6e3da41948190a4cfe866ce184f73 |
completed | May 3, 2026, 5:57 a.m. |
Created at: May 1, 2026, 1:36 a.m.