Triple
T6124418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nguyễn |
E136559
|
entity |
| Predicate | usedWithoutDiacriticsIn |
P51486
|
FINISHED |
| Object | international contexts |
—
|
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: international contexts | Statement: [Nguyễn, usedWithoutDiacriticsIn, international contexts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedWithoutDiacriticsIn Context triple: [Nguyễn, usedWithoutDiacriticsIn, international contexts]
-
A.
usesDiacritics
Indicates that the referenced text or linguistic element employs diacritical marks as part of its written form.
-
B.
diacriticStrippedForm
chosen
Indicates that one textual form is derived from another by removing all diacritic marks (such as accents or umlauts) from its characters.
-
C.
usesColloquialCharacters
Indicates that an expression, name, or text is written using informal, non-standard, or colloquial characters rather than formal or standard script.
-
D.
usesAlphabet
Indicates that one entity employs or is written using the alphabet or writing system associated with another entity.
-
E.
usesLatinAlphabetSince
Indicates that an entity has employed the Latin alphabet as its writing system starting from a specific point in time and continuing thereafter.
- 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_69c0089f851c81909e5e189a617dcff6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05c2791948190ba33458edfd1ebe8 |
completed | March 22, 2026, 9:16 p.m. |
| PD | Predicate disambiguation | batch_69c049f9ab3c81909c8ab6466f6a2935 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:14 p.m.