Triple
T1738465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ilia Kulik |
E37973
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Ilia Kulik |
E37973
|
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: Ilia Kulik | Statement: [Ilia Kulik, name, Ilia Kulik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ilia Kulik Context triple: [Ilia Kulik, name, Ilia Kulik]
-
A.
Ilia Kulik
chosen
Ilia Kulik is a Russian figure skater best known for winning the men's singles gold medal at the 1998 Winter Olympics.
-
B.
Daniil Granin
Daniil Granin was a prominent Soviet and Russian writer and public figure known for his novels about World War II and moral responsibility in science and society.
-
C.
Kirill Shubsky
Kirill Shubsky is a Russian businessman known primarily as the husband of actress and model Anastasia Shubskaya.
-
D.
Ondrej Nepela
Ondrej Nepela was a Slovak figure skater and 1972 Olympic champion, regarded as one of the most successful skaters of his era.
-
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.
- 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_69a8861cc6ac8190ac0b2e31ccf62851 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa63c35aec8190b5c19ace5524173f |
completed | March 6, 2026, 5:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad8b03303c8190a301dca327bf9f47 |
completed | March 8, 2026, 2:43 p.m. |
Created at: March 4, 2026, 7:30 p.m.