Triple
T17417899
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catholic Diocese of Turku |
E423530
|
entity |
| Predicate | layLanguage |
P72909
|
FINISHED |
| Object | Finnish |
—
|
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: Finnish | Statement: [Catholic Diocese of Turku, layLanguage, Finnish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: layLanguage Context triple: [Catholic Diocese of Turku, layLanguage, Finnish]
-
A.
languageUse
Indicates the language or languages an entity uses for communication, expression, or interaction.
-
B.
typicalLanguageUse
chosen
Indicates that one entity is the language most commonly or habitually used by another entity in ordinary communication or contexts.
-
C.
languageStandard
Indicates that one entity conforms to, is defined by, or is governed by the language rules, specifications, or conventions established by another entity as a standard.
-
D.
linguisticUsage
Indicates how a linguistic form, expression, or construction is used in language, such as its typical context, function, or register.
-
E.
languageEmphasizes
Indicates that one language or linguistic system places particular focus, importance, or prominence on a specific feature, concept, or element compared to others.
- 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_69d889d7d27c819088486ce3f0627fa1 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e44233c7888190a4d2aa703b206851 |
completed | April 19, 2026, 2:47 a.m. |
| PD | Predicate disambiguation | batch_69e3b02e6cc88190986e85e64ce9383e |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:46 a.m.