Triple
T1241627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kannada script |
E26668
|
entity |
| Predicate | hasVowelLettersCount |
P4430
|
FINISHED |
| Object | 13 |
—
|
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: 13 | Statement: [Kannada script, hasVowelLettersCount, 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVowelLettersCount Context triple: [Kannada script, hasVowelLettersCount, 13]
-
A.
hasNumberOfVowelLetters
chosen
Indicates that an entity is associated with a specific count of vowel letters it contains.
-
B.
containsVowelLetters
Indicates that the subject includes one or more vowel letters within its sequence of characters.
-
C.
hasDistinctVowelLetters
Indicates that the subject contains vowel letters that are all different from one another, with no vowel repeated.
-
D.
hasNumberOfConsonantLetters
Indicates the relationship between an entity and the count of consonant letters present in its written form.
-
E.
hasIndependentVowelLetters
Indicates that a writing system includes separate, standalone vowel characters rather than representing vowels only through modifications of consonant letters.
- 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_69a4948689d08190b3a4a3f388c02148 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf44ac3c8190a28a333b320305fd |
completed | March 1, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69a4bb696a38819095845c84f0241287 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:47 p.m.