Triple
T2268152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malayalam script |
E50594
|
entity |
| Predicate | hasNumberOfVowelSigns |
P37564
|
FINISHED |
| Object | more than 15 |
—
|
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: more than 15 | Statement: [Malayalam script, hasNumberOfVowelSigns, more than 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfVowelSigns Context triple: [Malayalam script, hasNumberOfVowelSigns, more than 15]
-
A.
hasNumberOfVowelLetters
Indicates that an entity is associated with a specific count of vowel letters it contains.
-
B.
hasDistinctVowelLetters
Indicates that the subject contains vowel letters that are all different from one another, with no vowel repeated.
-
C.
hasVowelFeature
Indicates that an entity possesses a specific vowel-related phonological or articulatory feature.
-
D.
containsVowelLetters
Indicates that the subject includes one or more vowel letters within its sequence of characters.
-
E.
hasVow
Indicates that one entity has made or is bound by a formal vow or promise in relation to another entity or context.
- 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_69a88b05910c8190a9a2b1ff230c85f9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc2ea65288190bc8644a07a11dfa9 |
completed | March 7, 2026, 6:17 a.m. |
| PD | Predicate disambiguation | batch_69abbdb592588190ac1ef5e8c54575b1 |
completed | March 7, 2026, 5:55 a.m. |
| PDg | Predicate description generation | batch_69abc2e97eb0819084acb26cfa4e3946 |
completed | March 7, 2026, 6:17 a.m. |
Created at: March 4, 2026, 7:48 p.m.