Triple
T246859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Telugu |
E5056
|
entity |
| Predicate | scriptUnicodeBlock |
P1445
|
FINISHED |
| Object | Telugu (U+0C00–U+0C7F) |
—
|
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: Telugu (U+0C00–U+0C7F) | Statement: [Telugu, scriptUnicodeBlock, Telugu (U+0C00–U+0C7F)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scriptUnicodeBlock Context triple: [Telugu, scriptUnicodeBlock, Telugu (U+0C00–U+0C7F)]
-
A.
UnicodeBlock
chosen
Indicates that a character belongs to a specific contiguous range of code points defined as a Unicode block.
-
B.
hasUnicodeScript
Indicates that a character or text element belongs to a specific Unicode script category (such as Latin, Cyrillic, or Han).
-
C.
basicMultilingualPlaneRange
Indicates that the referenced value or code point range lies within the Basic Multilingual Plane (BMP) of the Unicode character set.
-
D.
writingSystem
Indicates that one entity is the script or system of written symbols used to represent the language or content of another entity.
-
E.
isMostWidelyUsedWritingSystem
Indicates that the subject writing system is used by more people or in more contexts than any other writing system.
- 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_69a257c4bf688190a46ebbf411ab7473 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d13b8088190a3f48f0388d57496 |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b63b0bc8190864d7324d339fb48 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:54 a.m.