Triple
T11961294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lebesgue measure |
E284674
|
entity |
| Predicate | isOuterRegularOnBorelSets |
P102504
|
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, isOuterRegularOnBorelSets, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isOuterRegularOnBorelSets Context triple: [Lebesgue measure, isOuterRegularOnBorelSets, true]
-
A.
isBaireCategory
Indicates that a space (or set) satisfies the Baire category property, meaning countable intersections of dense open subsets remain dense in that space.
-
B.
areRegularIn
Indicates that entities participate in or occur within a context, pattern, or structure in a consistent, uniform, and rule-governed manner.
-
C.
hasLebesgueMeasure
Indicates that a set is assigned a specific value by the Lebesgue measure, representing its "size" in the sense of measure theory.
-
D.
hasRegularity
Indicates that one entity exhibits a consistent, recurring pattern or uniform behavior with respect to another entity or over time.
-
E.
isClosedAndNowhereDense
Indicates that a set is both closed (contains all its limit points) and nowhere dense (its closure has empty interior, so it is "small" in the topological sense).
- 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.