Triple
T33710286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faculty of Humanities, Maria Curie-Skłodowska University |
E863715
|
entity |
| Predicate | mayOfferCoursesInLanguage |
P29959
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Faculty of Humanities, Maria Curie-Skłodowska University, mayOfferCoursesInLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayOfferCoursesInLanguage Context triple: [Faculty of Humanities, Maria Curie-Skłodowska University, mayOfferCoursesInLanguage, English]
-
A.
mayBeOfferedFor
Indicates that one entity can potentially be provided or made available as an option to another entity.
-
B.
alsoUsesLanguageOfInstruction
Indicates that an entity, in addition to its primary language, uses the same language that is designated as the language of instruction in a given context.
-
C.
languageOfTeachings
Indicates the language in which teachings, lessons, or instructional content are delivered or expressed.
-
D.
offersProgramsInLanguage
chosen
Indicates that an entity provides or conducts its programs using a specified language.
-
E.
hasLanguageOfStudy
Indicates that an entity studies or is engaged in learning a particular language.
- 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_69f3498844608190bb8f9b14908d2510 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6fab9ec108190a6879dcc45020aeb |
completed | May 3, 2026, 7:35 a.m. |
| PD | Predicate disambiguation | batch_69f6f96dd4c8819093d6a7bd046a9ad5 |
completed | May 3, 2026, 7:29 a.m. |
Created at: May 1, 2026, 1:43 a.m.