Triple
T14764465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sanjay Ghemawat |
E346957
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object | LevelDB |
E717513
|
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: LevelDB | Statement: [Sanjay Ghemawat, knownFor, LevelDB]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LevelDB Context triple: [Sanjay Ghemawat, knownFor, LevelDB]
-
A.
LevelDB
chosen
LevelDB is a fast, lightweight, open-source key–value storage library developed by Google, designed for high-performance reads and writes using a log-structured merge-tree architecture.
-
B.
RocksDB
RocksDB is a high-performance, embeddable key–value store developed by Facebook, optimized for fast storage on flash and solid-state drives using a Log-Structured Merge-Tree (LSM) architecture.
-
C.
BSTDB
BSTDB is a regional multilateral development bank that finances projects to promote economic development and cooperation among Black Sea region member countries.
-
D.
Yama LSM
Yama LSM is a Linux Security Module focused on strengthening process isolation and restricting ptrace and related debugging capabilities to harden the kernel against local attacks.
-
E.
Log-Structured Merge-Tree
A Log-Structured Merge-Tree (LSM-tree) is a write-optimized data structure that organizes data in sequential logs and periodically merges them to provide efficient writes and good read performance for key-value storage systems.
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7f3a1608190b1b17624003a0c7f |
completed | April 14, 2026, 11:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe0cf4cef081909fa62125f43b36bc |
completed | May 8, 2026, 4:19 p.m. |
Created at: April 10, 2026, 1:30 a.m.