Triple
T31744790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dover–Calais |
E810238
|
entity |
| Predicate | languageAtCalais |
P115774
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [Dover–Calais, languageAtCalais, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageAtCalais Context triple: [Dover–Calais, languageAtCalais, French]
-
A.
languageOfSurroundingCountry
Indicates that a language is the primary or commonly used language in the country surrounding a given place or region.
-
B.
isFrancophoneCounterpartOf
Indicates that one entity serves as the French-speaking or French-language equivalent or counterpart of another entity.
-
C.
isLinguaFrancaOf
Indicates that a language serves as a common medium of communication between speakers of different native languages within a particular region, community, or context.
-
D.
languagePair
Indicates a relationship that associates two specific languages as a paired combination, typically for translation, comparison, or mapping between them.
-
E.
languageUsedInLocality
chosen
Indicates that a particular language is used or spoken within a specific locality or geographic area.
- 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_69f348e233cc819083b6695f70cd75d8 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69ff9bed58dc8190a204816d4ed6c32c |
completed | May 9, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69ff9b69653c81908ab0d88055a66a88 |
completed | May 9, 2026, 8:39 p.m. |
Created at: April 30, 2026, 11:26 p.m.