Triple
T12025135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andrew Tridgell |
E286255
|
entity |
| Predicate | notableFor |
P22
|
FINISHED |
| Object | rsync algorithm |
E286256
|
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: rsync algorithm | Statement: [Andrew Tridgell, notableFor, rsync algorithm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: rsync algorithm Context triple: [Andrew Tridgell, notableFor, rsync algorithm]
-
A.
rsync algorithm
chosen
The rsync algorithm is a file synchronization and transfer method that efficiently updates files over a network by sending only the differences between source and destination.
-
B.
rsync
rsync is a widely used open-source utility for fast, incremental file transfer and synchronization across local and remote systems.
-
C.
Diffusing Update Algorithm
Diffusing Update Algorithm is the loop-free, distributed routing computation method at the core of EIGRP that enables rapid, efficient route convergence in IP networks.
-
D.
Rabin–Karp algorithm
The Rabin–Karp algorithm is a string-searching technique that uses hashing to efficiently find any one of a set of pattern strings in a text.
-
E.
Cristian's algorithm
Cristian's algorithm is a clock synchronization method in distributed systems that estimates accurate time on client machines by querying a time server and adjusting for message delays.
- 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_69d6ab4669e48190b59246358b0383ab |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903f02638819091e0cc0e93fa5ea7 |
completed | April 10, 2026, 2:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f48b71e5c48190a58ace8ef7c8928d |
completed | May 1, 2026, 11:16 a.m. |
Created at: April 8, 2026, 9:47 p.m.