Triple
T1275872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Corsi |
E27211
|
entity |
| Predicate | relatedLanguageName |
P10003
|
FINISHED |
| Object | Corsu |
—
|
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: Corsu | Statement: [Corsi, relatedLanguageName, Corsu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedLanguageName Context triple: [Corsi, relatedLanguageName, Corsu]
-
A.
linguisticallyRelatedTo
chosen
Indicates that two entities are connected through a linguistic relationship, such as sharing a common language, origin, structure, or other language-based association.
-
B.
recognizedAsDistinctLanguageFrom
Indicates that one language is formally acknowledged or treated as a separate and distinct language from another, rather than as a dialect or variant of it.
-
C.
influencedLanguage
Indicates that one language has had an effect on the development, structure, or usage of another language.
-
D.
hasNeighboringLanguages
Indicates that two languages are geographically or regionally adjacent to each other in their areas of use.
-
E.
languageFamilyAssociated
Indicates that there is an association or connection between a language and a particular language family.
- 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_69a496d3710c8190955dee8bc0dacb50 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c31602b8819087a57e8d390cae7a |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4bee0be808190a8ccac6a41851fdd |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:50 p.m.