Triple
T8586894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regional Offices of the Finnish Defence Forces |
E203327
|
entity |
| Predicate | hasAlternativeLanguage |
P63334
|
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: [Regional Offices of the Finnish Defence Forces, hasAlternativeLanguage, Swedish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAlternativeLanguage Context triple: [Regional Offices of the Finnish Defence Forces, hasAlternativeLanguage, Swedish]
-
A.
hasLanguages
Indicates that an entity is associated with one or more languages it uses, supports, or is expressed in.
-
B.
alternateLanguageName
chosen
Indicates that an entity has an additional name or label in a different language from its primary or default name.
-
C.
hasSecondaryLanguage
Indicates that an entity possesses or uses a secondary language in addition to its primary language.
-
D.
hasAlternativeVocalization
Indicates that an entity has another valid way it can be vocalized or pronounced, distinct from its primary or standard vocalization.
-
E.
hasLanguageOn
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
- 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_69ca8329bb7c8190a63c643730839103 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46c5e8888190b721e791c449b0df |
completed | March 31, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69cc454504448190aaad2af8b17357cd |
completed | March 31, 2026, 10:05 p.m. |
Created at: March 30, 2026, 6:23 p.m.