Triple
T14393633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gregory Piatetsky-Shapiro |
E356903
|
entity |
| Predicate | founded |
P104
|
FINISHED |
| Object | KDD conference series |
E13737
|
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: KDD conference series | Statement: [Gregory Piatetsky-Shapiro, founded, KDD conference series]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KDD conference series Context triple: [Gregory Piatetsky-Shapiro, founded, KDD conference series]
-
A.
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.
-
B.
SIGKDD
chosen
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.
-
C.
IEEE International Conference on Data Mining
The IEEE International Conference on Data Mining is a leading annual research conference that focuses on advances in data mining, machine learning, and knowledge discovery in databases.
-
D.
IEEE International Conference on Data Engineering
The IEEE International Conference on Data Engineering is a leading annual research conference focused on advances in data management, database systems, and related engineering technologies.
-
E.
Mining of Massive Datasets
"Mining of Massive Datasets" is a widely used textbook that introduces practical and scalable data mining and machine learning techniques for analyzing large-scale datasets.
- 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_69fd8aa530fc81908aecc4439eea4c01 |
completed | May 8, 2026, 7:03 a.m. |
Created at: April 10, 2026, 1:16 a.m.