Triple
T11961293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lebesgue measure |
E284674
|
entity |
| Predicate | isRegular |
P102503
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Lebesgue measure, isRegular, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isRegular Context triple: [Lebesgue measure, isRegular, true]
-
A.
areRegularIn
Indicates that entities participate in or occur within a context, pattern, or structure in a consistent, uniform, and rule-governed manner.
-
B.
isRegularAt
Indicates that a function or mapping behaves regularly (e.g., is analytic, smooth, or non-singular) at a specified point or region, without irregularities or singularities there.
-
C.
hasRegularity
Indicates that one entity exhibits a consistent, recurring pattern or uniform behavior with respect to another entity or over time.
-
D.
isRegularChange
Indicates that a change occurs with consistent, recurring frequency or pattern over time.
-
E.
isRegularMap
Indicates that a mapping between two mathematical structures preserves the required regularity conditions (such as continuity, differentiability, or algebraic regularity) specified for that context.
- 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_69d6ab2eaeb881909f7914758f859413 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9036941948190b150369094551731 |
completed | April 10, 2026, 2:04 p.m. |
| PD | Predicate disambiguation | batch_69d8bb40f30c8190a0e0719bd67542bf |
completed | April 10, 2026, 8:56 a.m. |
| PDg | Predicate description generation | batch_69d8dd0ba0f88190b7d5e358c27ca184 |
completed | April 10, 2026, 11:20 a.m. |
Created at: April 8, 2026, 9:45 p.m.