Triple
T7150573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hartree–Fock method |
E166679
|
entity |
| Predicate | computationalScaling |
P64004
|
FINISHED |
| Object | approximately O(N^4) with system size |
—
|
LITERAL 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: approximately O(N^4) with system size | Statement: [Hartree–Fock method, computationalScaling, approximately O(N^4) with system size]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: computationalScaling Context triple: [Hartree–Fock method, computationalScaling, approximately O(N^4) with system size]
-
A.
computationalClass
Indicates that two entities share the same computational complexity class or that one entity is categorized within a specified computational complexity class.
-
B.
computationalCost
chosen
Indicates the amount of computing resources (such as time, memory, or processing power) required to perform a given operation or process.
-
C.
computes
Indicates that one entity performs a calculation or processing operation to produce a result from given data or inputs.
-
D.
usesComputationMethod
Indicates that an entity performs its processing or decision-making by applying a specified computational method or algorithm.
-
E.
scalability
Indicates the capacity of a system, process, or solution to handle increasing amounts of work, users, or data by efficiently expanding its resources or performance.
- F. None of above.
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_69c68886779c8190a8e3fbabffe68253 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e7f28b188190b1732ca711666531 |
completed | March 27, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69c6e1caf4e48190b47bb398a3c1554d |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:46 p.m.