Triple
T720238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Balinese script |
E14599
|
entity |
| Predicate | hasIndependentVowelLetters |
P18517
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Balinese script, hasIndependentVowelLetters, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIndependentVowelLetters Context triple: [Balinese script, hasIndependentVowelLetters, true]
-
A.
hasDependentVowels
Indicates that a writing system or script uses vowel signs that are attached to or depend on consonant characters rather than standing alone as independent letters.
-
B.
containsVowelLetters
Indicates that the subject includes one or more vowel letters within its sequence of characters.
-
C.
hasNumberOfVowelLetters
Indicates that an entity is associated with a specific count of vowel letters it contains.
-
D.
hasVowelHarmony
Indicates that the phonological vowels in a word or morpheme conform to a systematic harmony pattern (e.g., all front or all back vowels) according to the language’s vowel harmony rules.
-
E.
hasNasalVowels
Indicates that the subject language or phonological system includes vowels that are produced with nasal airflow (nasalized vowels).
- 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_69a4934c753c81909b309027e48b9b3a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a58e65e8819098cba7e6a20d8f33 |
completed | March 1, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69a4a4f513608190b716b939d574c292 |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a57267c481909790a1fda3fced08 |
completed | March 1, 2026, 8:45 p.m. |
Created at: March 1, 2026, 7:37 p.m.