Triple
T6096404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grantha (Unicode block) |
E135887
|
entity |
| Predicate | hasCanonicalCombiningClassMarks |
P68104
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Grantha (Unicode block), hasCanonicalCombiningClassMarks, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCanonicalCombiningClassMarks Context triple: [Grantha (Unicode block), hasCanonicalCombiningClassMarks, yes]
-
A.
hasCombiningMarks
Indicates that an entity (such as a character or string) includes one or more combining marks attached to a base element.
-
B.
hasCanonicalCharacter
Indicates that something is associated with or defined by its standard, officially recognized character representation.
-
C.
hasUnicodeCodePoint
Indicates that a character or symbol is associated with a specific numeric Unicode code point value.
-
D.
hasBlockUnicode
Indicates that one entity possesses or is associated with a specific Unicode block related to another entity.
-
E.
hasUnicodeScript
Indicates that a character or text element belongs to a specific Unicode script category (such as Latin, Cyrillic, or Han).
- F. None of above. chosen
Provenance (4 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_69c0087cd3c48190b459848c72d84eb1 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05a9764048190ad4e9a02f9a25ab6 |
completed | March 22, 2026, 9:09 p.m. |
| PD | Predicate disambiguation | batch_69c049f5ac988190b62ba565153aaa35 |
completed | March 22, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69c04e8e3f2c8190be459ca02f9b315a |
completed | March 22, 2026, 8:18 p.m. |
Created at: March 22, 2026, 4:12 p.m.