Triple
T21592171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Koskinen |
E532807
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Timo Koskinen
Timo Koskinen is a Finnish individual notable enough to be recognized as a bearer of the surname Koskinen.
|
E1495145
|
NE FINISHED |
How this triple was built (4 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: Timo Koskinen | Statement: [Koskinen, hasNotableBearer, Timo Koskinen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Timo Koskinen Context triple: [Koskinen, hasNotableBearer, Timo Koskinen]
-
A.
Timo Kivinen
Timo Kivinen is a Finnish general who has served as the Commander of the Finnish Defence Forces.
-
B.
Timo Sarpaneva
Timo Sarpaneva was a renowned Finnish designer and glass artist celebrated for his innovative, modernist creations that helped define Scandinavian design in the 20th century.
-
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.
Jari Koskinen
Jari Koskinen is a Finnish politician known for his roles in national and local government, including serving as a member of the Parliament of Finland.
-
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. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Timo Koskinen Triple: [Koskinen, hasNotableBearer, Timo Koskinen]
Generated description
Timo Koskinen is a Finnish individual notable enough to be recognized as a bearer of the surname Koskinen.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Timo Koskinen Target entity description: Timo Koskinen is a Finnish individual notable enough to be recognized as a bearer of the surname Koskinen.
-
A.
Timo Kivinen
Timo Kivinen is a Finnish general who has served as the Commander of the Finnish Defence Forces.
-
B.
Timo Sarpaneva
Timo Sarpaneva was a renowned Finnish designer and glass artist celebrated for his innovative, modernist creations that helped define Scandinavian design in the 20th century.
-
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.
Jari Koskinen
Jari Koskinen is a Finnish politician known for his roles in national and local government, including serving as a member of the Parliament of Finland.
-
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. chosen
Provenance (5 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_69e0c46251648190876f0427cf2d321b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eefadeb56c8190bce79efadf3c644d |
completed | April 27, 2026, 5:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a158661788190a6d3f526b9a094a7 |
completed | May 17, 2026, 7:22 p.m. |
| NEDg | Description generation | batch_6a0a168d3cbc81909a2a7a2c3e344ac7 |
completed | May 17, 2026, 7:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a1704fb4c819086a689ccced74f90 |
completed | May 17, 2026, 7:29 p.m. |
Created at: April 16, 2026, 6:32 p.m.