Triple
T18480230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orthogonal Polynomials |
E451537
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Gábor Szegő |
—
|
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: Gábor Szegő | Statement: [Orthogonal Polynomials, author, Gábor Szegő]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gábor Szegő Context triple: [Orthogonal Polynomials, author, Gábor Szegő]
-
A.
Gábor Szegő
chosen
Gábor Szegő was a Hungarian-American mathematician renowned for his contributions to analysis, particularly in the theory of orthogonal polynomials and Toeplitz matrices.
-
B.
Lipót Fejér
Lipót Fejér was a Hungarian mathematician renowned for his fundamental contributions to harmonic analysis and Fourier series, and for mentoring a generation of influential mathematicians.
-
C.
Béla Szőkefalvi-Nagy
Béla Szőkefalvi-Nagy was a Hungarian mathematician renowned for his contributions to functional analysis and operator theory.
-
D.
Pál Kalmár
Pál Kalmár was a Hungarian singer best known for his early and influential recording of the melancholic song "Gloomy Sunday."
-
E.
Pólya György
Pólya György was a Hungarian mathematician renowned for his work in problem solving, combinatorics, and mathematical education, and for authoring the classic book "How to Solve It."
- 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_69d8d38465a0819099b9b42d2a662ac1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53066a7108190a50eda9b489c90ca |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 11:35 a.m.