Triple
T27091910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ferrarese dialect |
E686188
|
entity |
| Predicate | hasNeighborDialect |
P16383
|
FINISHED |
| Object | Bolognese dialect |
—
|
NE NERFINISHED |
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: Bolognese dialect | Statement: [Ferrarese dialect, hasNeighborDialect, Bolognese dialect]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNeighborDialect Context triple: [Ferrarese dialect, hasNeighborDialect, Bolognese dialect]
-
A.
hasNeighboringLanguages
chosen
Indicates that two languages are geographically or regionally adjacent to each other in their areas of use.
-
B.
hasDialectalCounterpart
Indicates that one linguistic form has a corresponding equivalent or variant in another dialect.
-
C.
hasNumberOfDialects
Indicates the relationship between a language (or linguistic entity) and the count of distinct dialects it possesses.
-
D.
hasDialectsIn
Indicates that a language or linguistic variety possesses distinct dialects that are used or found within a specified region or context.
-
E.
hasDialectalDifferenceWith
Indicates that two language varieties differ from each other in dialectal features such as pronunciation, vocabulary, or grammar.
- 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_69ef148940ec819097b5c20fbfbf7c81 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69fbad1e94988190b86d447a68e65067 |
completed | May 6, 2026, 9:05 p.m. |
| PD | Predicate disambiguation | batch_69fba881b8e0819094790935152b99a1 |
completed | May 6, 2026, 8:45 p.m. |
Created at: April 27, 2026, 8:41 a.m.