Triple
T23805892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nynäshamn–Gdańsk route |
E589705
|
entity |
| Predicate | languageAtTerminus |
P153622
|
FINISHED |
| Object | Swedish |
—
|
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: Swedish | Statement: [Nynäshamn–Gdańsk route, languageAtTerminus, Swedish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageAtTerminus Context triple: [Nynäshamn–Gdańsk route, languageAtTerminus, Swedish]
-
A.
primaryLanguageAtBritishTerminus
Indicates that a specified language is the main or dominant language used at a particular British terminus location.
-
B.
languageCategory
Indicates the classification relationship where a language is assigned to a particular linguistic or functional category.
-
C.
standardLanguageOf
Indicates that one entity serves as the officially recognized or commonly used standard language for another entity (such as a country, region, or organization).
-
D.
languageLabel
Indicates the human-readable name or label of a language associated with an entity or resource.
-
E.
languageOfSurroundingCountry
Indicates that a language is the primary or commonly used language in the country surrounding a given place or region.
- F. None of above. chosen
Provenance (4 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_69e25d19fecc8190a5cf39bbb18d5d7f |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1c75206a88190b6e35da289471c27 |
completed | April 29, 2026, 8:54 a.m. |
| PD | Predicate disambiguation | batch_69f155fe300481909bd617443228df65 |
completed | April 29, 2026, 12:51 a.m. |
| PDg | Predicate description generation | batch_69f15adb23d88190ac2632299c26a9b3 |
completed | April 29, 2026, 1:11 a.m. |
Created at: April 17, 2026, 7:56 p.m.