Triple
T4275077
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aleksander Barkov |
E97030
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Barkov
Barkov is a surname most prominently associated with Aleksander Barkov, a Finnish professional ice hockey player and NHL star.
|
E427863
|
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: Barkov | Statement: [Aleksander Barkov, familyName, Barkov]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barkov Context triple: [Aleksander Barkov, familyName, Barkov]
-
A.
Kovalchuk
Kovalchuk is a common East Slavic surname, notably borne by several professional ice hockey players and other public figures from Russia, Ukraine, and Belarus.
-
B.
Modano
Modano is the surname of Mike Modano, a Hall of Fame American ice hockey player widely regarded as one of the greatest U.S.-born NHL forwards.
-
C.
Kirill Shubsky
Kirill Shubsky is a Russian businessman known primarily as the husband of actress and model Anastasia Shubskaya.
-
D.
Evgeni
Evgeni is a masculine given name most notably associated with Russian-born NHL star Evgeni Malkin.
-
E.
Igor Babuschkin
Igor Babuschkin is an AI researcher and engineer known for his work on large language models at organizations such as DeepMind, OpenAI, and later xAI.
- 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: Barkov Triple: [Aleksander Barkov, familyName, Barkov]
Generated description
Barkov is a surname most prominently associated with Aleksander Barkov, a Finnish professional ice hockey player and NHL star.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Barkov Target entity description: Barkov is a surname most prominently associated with Aleksander Barkov, a Finnish professional ice hockey player and NHL star.
-
A.
Kovalchuk
Kovalchuk is a common East Slavic surname, notably borne by several professional ice hockey players and other public figures from Russia, Ukraine, and Belarus.
-
B.
Modano
Modano is the surname of Mike Modano, a Hall of Fame American ice hockey player widely regarded as one of the greatest U.S.-born NHL forwards.
-
C.
Kirill Shubsky
Kirill Shubsky is a Russian businessman known primarily as the husband of actress and model Anastasia Shubskaya.
-
D.
Evgeni
Evgeni is a masculine given name most notably associated with Russian-born NHL star Evgeni Malkin.
-
E.
Igor Babuschkin
Igor Babuschkin is an AI researcher and engineer known for his work on large language models at organizations such as DeepMind, OpenAI, and later xAI.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501c35688190a7d15d904f15f968 |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5b7b0b2ec819090ccf042917ae207 |
completed | March 14, 2026, 7:32 p.m. |
| NEDg | Description generation | batch_69b5b8ac37c88190ad2c6a8358e17554 |
completed | March 14, 2026, 7:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5b92eec8c81908c114238af39450f |
completed | March 14, 2026, 7:38 p.m. |
Created at: March 12, 2026, 11:07 p.m.