Triple
T64783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vietnamese alphabet |
E1288
|
entity |
| Predicate | hasNumberOfConsonantLetters |
P4431
|
FINISHED |
| Object | 17 |
—
|
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: 17 | Statement: [Vietnamese alphabet, hasNumberOfConsonantLetters, 17]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfConsonantLetters Context triple: [Vietnamese alphabet, hasNumberOfConsonantLetters, 17]
-
A.
hasBasicLetters
Indicates that an entity contains or is composed of fundamental alphabetic characters, without additional symbols or diacritics.
-
B.
hasStandardLetterCount
Indicates that an entity’s associated text or label contains a number of letters that matches a predefined standard or expected count.
-
C.
hasNumberOfCasesApprox
Indicates that an entity is associated with an approximate (not exact) count of cases.
-
D.
hasSpecialCharacter
Indicates that a given entity (such as a string or identifier) contains at least one non-alphanumeric special character.
-
E.
hasPostnominalLetters
Indicates that a person holds specific postnominal letters (abbreviations after their name) signifying qualifications, honors, or titles.
- 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_69a24ba4f760819081f6638a3c70538a |
completed | Feb. 28, 2026, 1:57 a.m. |
| NER | Named-entity recognition | batch_69a2516eda54819090f5c14384d4eab1 |
completed | Feb. 28, 2026, 2:22 a.m. |
| PD | Predicate disambiguation | batch_69a24ea5c140819080409a968c8d2ce8 |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a2516d98e88190b79261bd3fcadd9b |
completed | Feb. 28, 2026, 2:22 a.m. |
Created at: Feb. 28, 2026, 2:02 a.m.