Triple
T1788059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Devanagari Extended-A |
E39432
|
entity |
| Predicate | encodingModel |
P7030
|
FINISHED |
| Object | character-based encoding |
—
|
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: character-based encoding | Statement: [Devanagari Extended-A, encodingModel, character-based encoding]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: encodingModel Context triple: [Devanagari Extended-A, encodingModel, character-based encoding]
-
A.
encodingStructure
Indicates the structural scheme or format used to encode information or data.
-
B.
encodes
Indicates that one entity contains or represents the information, instructions, or structure of another in a coded or symbolic form.
-
C.
encodedIn
Indicates that one entity is represented, stored, or expressed within another entity using a specific encoding or format.
-
D.
model
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
-
E.
embeddingType
chosen
Indicates the specific kind or category of embedding representation used to encode an entity or data.
- 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_69a88631854081909723959921e45c2b |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab75457e54819096b8c6ae8c65550c |
completed | March 7, 2026, 12:45 a.m. |
| PD | Predicate disambiguation | batch_69aa61d165688190924962a98e07ff69 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:32 p.m.