Triple
T11955298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romania and Ukraine |
E284535
|
entity |
| Predicate | haveLanguageContact |
P22730
|
FINISHED |
| Object | Romanian and Ukrainian |
—
|
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: Romanian and Ukrainian | Statement: [Romania and Ukraine, haveLanguageContact, Romanian and Ukrainian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveLanguageContact Context triple: [Romania and Ukraine, haveLanguageContact, Romanian and Ukrainian]
-
A.
hasContactWithLanguage
Indicates that an entity has some form of interaction, exposure, or engagement with a particular language.
-
B.
languageContactWith
chosen
Indicates a relationship where two or more languages come into contact through their speakers, leading to interaction and potential mutual influence.
-
C.
hasLanguageInhabitants
Indicates that a particular language is spoken or used by the inhabitants of a specified place or region.
-
D.
usedAsContactLanguageBetween
Indicates that a language functions as the medium of communication between two or more distinct language communities.
-
E.
hasLanguageOfSurroundingCountries
Indicates that an entity uses or includes the languages commonly spoken in the countries that geographically surround it.
- 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90366fda8819083168c93abad27d4 |
completed | April 10, 2026, 2:04 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3e48e08190b2fee43af4f57323 |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:45 p.m.