Triple
T10852563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vamma power station |
E256183
|
entity |
| Predicate | hasOfficialLanguageOfLocation |
P96074
|
FINISHED |
| Object | Norwegian |
—
|
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: Norwegian | Statement: [Vamma power station, hasOfficialLanguageOfLocation, Norwegian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOfficialLanguageOfLocation Context triple: [Vamma power station, hasOfficialLanguageOfLocation, Norwegian]
-
A.
hasOfficialCountryLanguage
Indicates that a country recognizes a particular language as one of its official languages for governmental or legal purposes.
-
B.
hasOfficialLanguageOfSurroundingCountry
Indicates that an entity uses as its official language the same language that is official in the country surrounding it.
-
C.
hasLanguageOfOfficialName
Indicates that an entity’s official name is expressed in a specified language.
-
D.
isUNOfficialLanguage
Indicates that a language holds official status within the United Nations.
-
E.
shareOfficialLanguage
Indicates that two entities have at least one official language in common.
- 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_69d6aa83d1448190a66d93c32394d21f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75134299481909459fd87917261a7 |
completed | April 9, 2026, 7:11 a.m. |
| PD | Predicate disambiguation | batch_69d70d2b51448190bae748ed6c23edde |
completed | April 9, 2026, 2:21 a.m. |
| PDg | Predicate description generation | batch_69d7101c96708190808fef73199e8482 |
completed | April 9, 2026, 2:34 a.m. |
Created at: April 8, 2026, 9:20 p.m.