Triple
T14393632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gregory Piatetsky-Shapiro |
E356903
|
entity |
| Predicate | founded |
P104
|
FINISHED |
| Object | KDnuggets |
E1096530
|
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: KDnuggets | Statement: [Gregory Piatetsky-Shapiro, founded, KDnuggets]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KDnuggets Context triple: [Gregory Piatetsky-Shapiro, founded, KDnuggets]
-
A.
KDnuggets
chosen
KDnuggets is a prominent online platform and newsletter focused on data science, machine learning, and analytics news, tutorials, and resources.
-
B.
KDD
KDD is the commonly used abbreviation for Norway’s Ministry of Local Government and Regional Development, a government body responsible for municipal affairs, regional policy, and housing.
-
C.
SIGKDD
SIGKDD is the ACM Special Interest Group on Knowledge Discovery and Data Mining, best known for its flagship KDD conference and contributions to data mining and machine learning research.
-
D.
JMLR
JMLR (Journal of Machine Learning Research) is a leading peer-reviewed academic journal focusing on cutting-edge research in machine learning and related fields.
-
E.
ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Knowledge Discovery from Data is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data mining, knowledge discovery, and related areas of data science and machine learning.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de902d114881908a8f3c01b3c6d309 |
completed | April 14, 2026, 7:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bc2836c8190a61dfd04127fd255 |
completed | May 8, 2026, 3:42 a.m. |
Created at: April 10, 2026, 1:16 a.m.