Triple
T9838710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | de Bruijn sequence |
E239166
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | de Bruijn graph |
E239167
|
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: de Bruijn graph | Statement: [de Bruijn sequence, relatedTo, de Bruijn graph]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: de Bruijn graph Context triple: [de Bruijn sequence, relatedTo, de Bruijn graph]
-
A.
de Bruijn graph
chosen
A de Bruijn graph is a directed graph structure that compactly represents overlaps between sequences of symbols, widely used in combinatorics, coding theory, and genome assembly algorithms.
-
B.
de Bruijn sequence
A de Bruijn sequence is a cyclic sequence over a given alphabet in which every possible subsequence of a fixed length appears exactly once.
-
C.
DAG
DAG is the National Rail station code for Dalgety Bay railway station in Fife, Scotland.
-
D.
Aho–Corasick algorithm
The Aho–Corasick algorithm is a classic string-searching algorithm that efficiently finds all occurrences of multiple patterns in a text using a trie-based finite-state machine.
-
E.
Graph Algorithms (book)
"Graph Algorithms" is a foundational textbook by Shimon Even that systematically presents the theory, design, and analysis of algorithms for solving fundamental problems on graphs.
- 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_69ca84e314108190978324a4bdb959f8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb34921b881909836ba0f5b42a27b |
completed | April 2, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d5d145ac8190ad10a4328216ef54 |
completed | April 5, 2026, 3:24 a.m. |
Created at: March 30, 2026, 8:33 p.m.