Triple
T8640829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erdős–Rényi model |
E204641
|
entity |
| Predicate | clusteringCoefficient |
P29146
|
FINISHED |
| Object | approximately equal to p |
—
|
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 equal to p | Statement: [Erdős–Rényi model, clusteringCoefficient, approximately equal to p]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: clusteringCoefficient Context triple: [Erdős–Rényi model, clusteringCoefficient, approximately equal to p]
-
A.
clusterDensity
Indicates the degree to which elements within a cluster are closely packed or concentrated relative to its size or volume.
-
B.
junctionCountApprox
Indicates an approximate count of junctions or connection points involved in or associated with the given entities.
-
C.
CMeans
Indicates that one concept or entity conveys, expresses, or serves as the means by which another concept or entity is understood or represented.
-
D.
coefficientProperty
chosen
Indicates a relationship where one entity serves as a coefficient or scalar factor that quantitatively modifies or characterizes another entity or property.
-
E.
isDegreeOf
Indicates that one entity is an academic or professional degree held, pursued, or associated with another entity.
- 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_69ca834ca1c88190a11ffb0200342fac |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc47944d1c819081f448f14d04bf9d |
completed | March 31, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69cc455d6d448190a2da2a319ac78c37 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:28 p.m.