Triple
T22673835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Slutsky equation |
E560291
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Eugen Slutsky |
—
|
NE NERFINISHED |
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: Eugen Slutsky | Statement: [Slutsky equation, namedAfter, Eugen Slutsky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eugen Slutsky Context triple: [Slutsky equation, namedAfter, Eugen Slutsky]
-
A.
Eugen Slutsky
chosen
Eugen Slutsky was a Russian economist and statistician known for foundational contributions to consumer demand theory and the analysis of random processes in time series.
-
B.
Leonid Eduardovich Slutsky
Leonid Eduardovich Slutsky is a Russian politician who has served as a long-time State Duma deputy and leader of the Liberal Democratic Party of Russia (LDPR).
-
C.
Naum Slutsky
Naum Slutsky was a Ukrainian-born British sculptor and designer associated with the Bauhaus movement, known for his innovative metalwork and modernist aesthetic.
-
D.
Abram Slutsky
Abram Slutsky was a Soviet intelligence officer who headed the foreign department of the NKVD during the 1930s.
-
E.
Aleksandr Khinchin
Aleksandr Khinchin was a prominent Russian mathematician known for his foundational contributions to probability theory, number theory, and information theory.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2454bfd00819099115715a22cb057 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f178229e908190b696d14a93c11344 |
completed | April 29, 2026, 3:16 a.m. |
Created at: April 17, 2026, 3:10 p.m.