Triple
T18542270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lipót Fejér |
E453128
|
entity |
| Predicate | student |
P7251
|
FINISHED |
| Object | Pál Turán |
—
|
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: Pál Turán | Statement: [Lipót Fejér, student, Pál Turán]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pál Turán Context triple: [Lipót Fejér, student, Pál Turán]
-
A.
Pál Turán
chosen
Pál Turán was a Hungarian mathematician renowned for his influential work in number theory and combinatorics, including the development of Turán's theorem in extremal graph theory.
-
B.
Alfréd Rényi
Alfréd Rényi was a Hungarian mathematician renowned for his influential work in probability theory, information theory, and number theory.
-
C.
Pál Erdős
Pál Erdős was a highly prolific 20th-century Hungarian mathematician renowned for his extensive contributions to number theory, combinatorics, and discrete mathematics, as well as his famously collaborative working style.
-
D.
László Kalmár
László Kalmár was a Hungarian mathematician known as a pioneer of theoretical computer science and mathematical logic in Hungary.
-
E.
Béla Szőkefalvi-Nagy
Béla Szőkefalvi-Nagy was a Hungarian mathematician renowned for his contributions to functional analysis and operator 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_69d8d387b5548190aa030dad2cb4947e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e534b80fc081908488417787d1b166 |
completed | April 19, 2026, 8:02 p.m. |
Created at: April 10, 2026, 11:38 a.m.