Triple
T4402873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mikael Agricola |
E93656
|
entity |
| Predicate | languageWorkedIn |
P45484
|
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: [Mikael Agricola, languageWorkedIn, Finnish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageWorkedIn Context triple: [Mikael Agricola, languageWorkedIn, Finnish]
-
A.
hasWorkedInLanguage
chosen
Indicates that an entity has performed work or professional activities using a particular language.
-
B.
languageOfPrimaryProgramming
Indicates the programming language that is primarily used to implement or develop a given entity.
-
C.
usesWorkingLanguagesOf
Indicates that one entity employs or operates using the working languages associated with another entity.
-
D.
languageDesigned
Indicates that one entity created or developed the language used or associated with another entity.
-
E.
languageAssociation
Indicates an association or relationship between entities based on a language they use, represent, or are linked to.
- 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_69b345158c748190a2c040fce2da9980 |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b352d08a0c8190ac6c125df40eca75 |
completed | March 12, 2026, 11:57 p.m. |
| PD | Predicate disambiguation | batch_69b34f5b36a881909bf2e970aa523390 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:28 p.m.