Triple
T12413279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Klaus Mäkelä |
E296571
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Klaus Mäkelä |
E296571
|
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: Klaus Mäkelä | Statement: [Klaus Mäkelä, name, Klaus Mäkelä]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Klaus Mäkelä Context triple: [Klaus Mäkelä, name, Klaus Mäkelä]
-
A.
Klaus Mäkelä
chosen
Klaus Mäkelä is a Finnish conductor and cellist recognized as one of the leading young maestros in international classical music.
-
B.
Timo Jukola
Timo Jukola is one of the central Jukola brothers in Aleksis Kivi’s classic Finnish novel "Seven Brothers," known for his impulsive nature and role in the brothers’ tumultuous journey toward maturity and civilization.
-
C.
Tapio Mäkelä
Tapio Mäkelä is a Finnish cross-country skier who competed in the 1950s and won a gold medal in the 4 × 10 km relay at the 1952 Winter Olympics.
-
D.
Tapio Wirtanen
Tapio Wirtanen is a person whose given name is Tapio, likely of Finnish origin.
-
E.
Timo Aila
Timo Aila is a computer scientist and researcher at NVIDIA known for his influential work in computer graphics and deep learning, including co-developing the StyleGAN generative adversarial network architecture.
- 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_69d6ad9f464c81909db36d7e96e34b9e |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d6b0f9c8190813b6fe3f97570ac |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63f002b7c81909ee9d4ea3ea6d5f2 |
completed | May 2, 2026, 6:14 p.m. |
Created at: April 8, 2026, 9:55 p.m.