Triple
T20557384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ubykh |
E504753
|
entity |
| Predicate | vowelInventorySize |
P102310
|
FINISHED |
| Object | 2 or 3 vowel phonemes |
—
|
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: 2 or 3 vowel phonemes | Statement: [Ubykh, vowelInventorySize, 2 or 3 vowel phonemes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vowelInventorySize Context triple: [Ubykh, vowelInventorySize, 2 or 3 vowel phonemes]
-
A.
hasVowelInventorySize
chosen
Indicates that an entity is associated with a specific number of distinct vowel sounds in its phonological system.
-
B.
vowCount
Indicates the number of vows associated with or made within a given relationship, event, or context.
-
C.
hasNumberOfVowelLetters
Indicates that an entity is associated with a specific count of vowel letters it contains.
-
D.
hasNumberOfVowelSigns
Indicates the count of vowel signs associated with or present in a given linguistic unit (such as a character, syllable, or word).
-
E.
phonemeInventorySize
Indicates the number of distinct phonemes present in a language’s sound 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_69e0b4b6587c8190aee63dc7cff244ea |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a5df84088190848c7eb35564d8f9 |
completed | April 20, 2026, 10:17 p.m. |
| PD | Predicate disambiguation | batch_69e59ff0116c8190a163ff28ed01430a |
completed | April 20, 2026, 3:39 a.m. |
Created at: April 16, 2026, 11:38 a.m.