Triple
T4575452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UTF-7 |
E123130
|
entity |
| Predicate | encodesIndirectly |
P14248
|
FINISHED |
| Object | non-ASCII Unicode characters |
—
|
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: non-ASCII Unicode characters | Statement: [UTF-7, encodesIndirectly, non-ASCII Unicode characters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: encodesIndirectly Context triple: [UTF-7, encodesIndirectly, non-ASCII Unicode characters]
-
A.
encodes
chosen
Indicates that one entity contains or represents the information, instructions, or structure of another in a coded or symbolic form.
-
B.
encodedIn
Indicates that one entity is represented, stored, or expressed within another entity using a specific encoding or format.
-
C.
encodingBasisFor
Indicates that one encoding scheme serves as the foundational or reference basis for defining or interpreting another encoding.
-
D.
encodingScope
Indicates the range or extent of content or information that is covered, represented, or captured by a particular encoding.
-
E.
indirectImpactOn
Indicates that one entity affects another entity’s state, condition, or outcome through one or more intermediate factors rather than through a direct interaction.
- 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_69bd46466c7081909d07f36be2d08804 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd58dfe3508190b21836079e951a3c |
completed | March 20, 2026, 2:25 p.m. |
| PD | Predicate disambiguation | batch_69bd5228b70c8190ac48705e35a710c1 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:10 p.m.