Triple
T13479228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tuomas Kantelinen |
E318327
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Tuomas Kantelinen |
E318327
|
NE 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: Tuomas Kantelinen | Statement: [Tuomas Kantelinen, name, Tuomas Kantelinen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tuomas Kantelinen Context triple: [Tuomas Kantelinen, name, Tuomas Kantelinen]
-
A.
Tuomas Kantelinen
chosen
Tuomas Kantelinen is a Finnish film composer known for his orchestral scores for both international and Finnish cinema.
-
B.
Tuomas Jukola
Tuomas Jukola is the strong-willed eldest of the Jukola brothers and a central figure in Aleksis Kivi’s classic Finnish novel "Seven Brothers," embodying their rebellious yet ultimately maturing spirit.
-
C.
Teemu Hartikainen
Teemu Hartikainen is a Finnish professional ice hockey forward known for his strong play in the KHL and a brief stint in the NHL with the Edmonton Oilers.
-
D.
Jussi Pakkanen
Jussi Pakkanen is a Finnish software developer best known as the original author of the Meson build system.
-
E.
Jaakko Lehtinen
Jaakko Lehtinen is a computer graphics and machine learning researcher known for his influential work on generative models and neural rendering techniques.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d806b6bfec819089222715b2e86c8e |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf266c508190930d30776c09ce35 |
completed | April 12, 2026, 2:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7463340fc8190b1128bd1d26f91ab |
completed | May 3, 2026, 12:57 p.m. |
Created at: April 9, 2026, 9:42 p.m.