Triple
T21610209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jakob Steiner |
E533284
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Steiner tree problem |
—
|
NE NERFINISHED |
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: Steiner tree problem | Statement: [Jakob Steiner, notableWork, Steiner tree problem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steiner tree problem Context triple: [Jakob Steiner, notableWork, Steiner tree problem]
-
A.
Steiner tree problem
chosen
The Steiner tree problem is a classic optimization problem in combinatorial mathematics and computer science that seeks the shortest network of line segments connecting a given set of points, potentially adding extra intermediate points to minimize total length.
-
B.
Steiner forest problem
The Steiner forest problem is a combinatorial optimization problem that seeks a minimum-cost forest connecting specified pairs or groups of terminals in a graph, generalizing the classical Steiner tree problem to multiple disjoint terminal sets.
-
C.
Kruskal’s minimum spanning tree algorithm
Kruskal’s minimum spanning tree algorithm is a classic greedy graph algorithm that builds a minimum spanning tree by repeatedly adding the smallest-weight edge that does not create a cycle, typically implemented efficiently using a union–find data structure.
-
D.
Combinatorial Optimization: Algorithms and Complexity
Combinatorial Optimization: Algorithms and Complexity is a foundational textbook that systematically develops the theory and algorithms of combinatorial optimization, emphasizing computational complexity and algorithmic efficiency.
-
E.
Gale’s theorem on flows with convex costs
Gale’s theorem on flows with convex costs is a fundamental result in mathematical optimization and network flow theory that characterizes optimal flows in networks when the cost functions on edges are convex rather than linear.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c46411108190bba0d4176dffc9f3 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef17e7d1388190922a90cb91ec9fc4 |
completed | April 27, 2026, 8:01 a.m. |
Created at: April 16, 2026, 6:33 p.m.