Triple
T34302630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bellman–Ford algorithm |
E880220
|
entity |
| Predicate | numberOfRelaxationPasses |
P205393
|
FINISHED |
| Object | |V| - 1 |
—
|
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: |V| - 1 | Statement: [Bellman–Ford algorithm, numberOfRelaxationPasses, |V| - 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRelaxationPasses Context triple: [Bellman–Ford algorithm, numberOfRelaxationPasses, |V| - 1]
-
A.
hasRelaxation
Indicates that one entity provides or is associated with a relaxation, easing, or reduction of constraints, tension, or strictness in relation to another entity.
-
B.
typicalNumberOfCycles
Indicates the usual or characteristic count of cycles associated with an entity, process, or event.
-
C.
numberOfReconstructions
Indicates the count of times an entity has been reconstructed or rebuilt.
-
D.
lapsRequired
Indicates the number of laps that must be completed for an activity, event, or condition to be considered fulfilled.
-
E.
optimizationLevel
Indicates the degree or intensity to which a process, system, or solution has been refined to improve its performance or efficiency.
- F. None of above. chosen
Provenance (4 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_69f349b79f6c81909cb468c92c39c74d |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379fbe4a08190bfe65ebd141164e9 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c80ba448190853011097a151b7e |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:57 a.m.