Triple
T24583327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sirionó language |
E608310
|
entity |
| Predicate | hasOralNasalVowelContrast |
P7439
|
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: [Sirionó language, hasOralNasalVowelContrast, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOralNasalVowelContrast Context triple: [Sirionó language, hasOralNasalVowelContrast, yes]
-
A.
hasNasalVowels
chosen
Indicates that the subject language or phonological system includes vowels that are produced with nasal airflow (nasalized vowels).
-
B.
hasNasalConsonants
Indicates that the subject language or word includes one or more nasal consonant sounds in its phonological inventory or pronunciation.
-
C.
hasNasalHarmony
Indicates that a phonological process causes nasality in one segment to spread to or be shared with other segments within a word or domain.
-
D.
hasVowelLengthContrast
Indicates that a language distinguishes word meanings based on differences in the length (duration) of vowel sounds.
-
E.
hasPhonemicContrast
Indicates that two or more speech sounds are distinguished in a language by differences that change word meaning.
- 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_69e2c4ce89248190ad99e18f0638dfbb |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a984577881908c855f5e05756909 |
completed | April 30, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69f2a6c1f07081908edf0b521767e79b |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:29 a.m.