Triple
T2332387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lubyanka Building |
E44230
|
entity |
| Predicate | usedBy |
P260
|
FINISHED |
| Object | OGPU |
E85097
|
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: OGPU | Statement: [Lubyanka Building, usedBy, OGPU]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OGPU Context triple: [Lubyanka Building, usedBy, OGPU]
-
A.
GPU
chosen
The GPU (State Political Directorate) was the Soviet Union’s early secret police and intelligence agency that operated in the 1920s, overseeing political repression and internal security before later reorganizations.
-
B.
GPU
GPU is the vehicle registration code used on license plates for cars registered in Poland’s Pomeranian Voivodeship.
-
C.
NVIDIA CUDA
NVIDIA CUDA is a parallel computing platform and programming model that enables developers to use NVIDIA GPUs for general-purpose high-performance computing.
-
D.
NVIDIA Studio
NVIDIA Studio is a platform and suite of tools, drivers, and optimizations designed to enhance performance and reliability for creative and content creation workflows on NVIDIA GPUs.
-
E.
NVIDIA DGX
NVIDIA DGX is a line of high-performance, AI-optimized computing systems designed for training and deploying large-scale machine learning and deep learning models.
- 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_69a889132b488190bbb43ad4780ddd92 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abc66bd0f08190aad5f640cfa1c372 |
completed | March 7, 2026, 6:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae89773c88819087a294d7c0f90f73 |
completed | March 9, 2026, 8:48 a.m. |
Created at: March 4, 2026, 7:51 p.m.