Triple
T294178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Devanagari script |
E6056
|
entity |
| Predicate | hasVowelCount |
P4430
|
FINISHED |
| Object | 14 vowels (traditional count) |
—
|
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: 14 vowels (traditional count) | Statement: [Devanagari script, hasVowelCount, 14 vowels (traditional count)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVowelCount Context triple: [Devanagari script, hasVowelCount, 14 vowels (traditional count)]
-
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.
hasNumberOfConsonantLetters
Indicates the relationship between an entity and the count of consonant letters present in its written form.
-
D.
hasSyllableCount
Indicates that one entity (typically a word or phrase) possesses a specific number of syllables given by the other entity.
-
E.
hasVowelLengthContrast
Indicates that a language distinguishes word meanings based on differences in the length (duration) of vowel sounds.
- 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2e9e273f88190ac5355d1310376ed |
completed | Feb. 28, 2026, 1:13 p.m. |
| PD | Predicate disambiguation | batch_69a2e9368894819093eeae4347dfcc5a |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.