Triple
T32789306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yuri Matiyasevich |
E838583
|
entity |
| Predicate | associatedWithProblem |
P205159
|
FINISHED |
| Object | Hilbert’s tenth problem |
E208849
|
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: Hilbert’s tenth problem | Statement: [Yuri Matiyasevich, associatedWithProblem, Hilbert’s tenth problem]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithProblem Context triple: [Yuri Matiyasevich, associatedWithProblem, Hilbert’s tenth problem]
-
A.
associatedWithUse
Indicates a relationship where one entity is connected to or involved in the use or utilization of another entity.
-
B.
associatedWithReport
Indicates that an entity has a connection or linkage to a specific report, such as being referenced in, contributing to, or otherwise related to that report.
-
C.
associatedWithSee
Indicates a relationship where one entity is contextually or functionally linked to another through the act or concept of seeing or visual observation.
-
D.
associatedWithDrug
Indicates that an entity has a relevant relationship or connection to a specific drug, such as use, exposure, or involvement in its context.
-
E.
associatedSin
Indicates a relationship where one entity is linked or connected to a particular sin or wrongful act.
- 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_69f3493b83f48190be335cd42465cecf |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a34bcecd6e4819086d4fa932eae87ae |
completed | June 19, 2026, 3:52 a.m. |
| PD | Predicate disambiguation | batch_6a0379f0cbe481909b4b8fc6cbe297f0 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037cab06288190b093935f235ddff2 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:14 a.m.