Triple
T28630460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turkish Wiktionary |
E724625
|
entity |
| Predicate | isMultilingualResource |
P162562
|
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: [Turkish Wiktionary, isMultilingualResource, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isMultilingualResource Context triple: [Turkish Wiktionary, isMultilingualResource, true]
-
A.
isMultilingual
Indicates that an entity can understand and/or communicate in multiple languages.
-
B.
hasMultilingualPresence
chosen
Indicates that an entity maintains an active presence or representation in multiple languages.
-
C.
hasLanguageResources
Indicates that an entity possesses or provides resources related to a particular language, such as tools, materials, or services supporting its use or study.
-
D.
isMultilingualWithinChinese
Indicates that an entity uses or involves multiple distinct Chinese language varieties (e.g., dialects, scripts, or regional forms) within a Chinese linguistic context.
-
E.
isBilingual
Indicates that an entity is able to communicate fluently in two distinct languages.
- 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_69f01d8328c48190bc0e5f9b9b848582 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f65f7731e4819099d5bd3d915ee266 |
completed | May 2, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f65c2198208190a3954086c22cfcbf |
completed | May 2, 2026, 8:18 p.m. |
Created at: April 28, 2026, 4:37 a.m.