Triple
T19231083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stone–Weierstrass theorem |
E480871
|
entity |
| Predicate | typeOfDensity |
P135272
|
FINISHED |
| Object | uniform density |
—
|
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: uniform density | Statement: [Stone–Weierstrass theorem, typeOfDensity, uniform density]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfDensity Context triple: [Stone–Weierstrass theorem, typeOfDensity, uniform density]
-
A.
givesDensityOf
Indicates that one entity provides or specifies the density value of another entity.
-
B.
densityClass
Indicates the classification of an entity based on its density level or range.
-
C.
hasMeanDensity
Indicates that one entity possesses a specified average mass per unit volume (mean density).
-
D.
hasPopulationDensityType
Indicates the classification of an area based on how densely populated it is (e.g., urban, suburban, rural).
-
E.
typeOfUnit
Indicates that one entity specifies the kind or category of measurement unit that the other entity belongs to.
- 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_69d8e8ccb8f48190ad420098e74fb1db |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fa9ce5e081909df994841ce476d5 |
completed | April 20, 2026, 10:06 a.m. |
| PD | Predicate disambiguation | batch_69e4dcfae6f081909cc173cf71a5005c |
completed | April 19, 2026, 1:47 p.m. |
| PDg | Predicate description generation | batch_69e4debc39ac81908b7c5ef797046360 |
completed | April 19, 2026, 1:55 p.m. |
Created at: April 10, 2026, 1:25 p.m.