Triple
T6929660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cantor set |
E160400
|
entity |
| Predicate | isCompactInR |
P56994
|
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: [Cantor set, isCompactInR, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isCompactInR Context triple: [Cantor set, isCompactInR, true]
-
A.
isCompact
chosen
Indicates that an object or space has a small, efficiently arranged size or volume relative to its function or contents.
-
B.
isNoncompact
Indicates that the object (such as a space or set) lacks compactness, meaning it does not satisfy the property that every open cover has a finite subcover.
-
C.
isLocallyCompact
Indicates that a topological space has the property that every point has a neighborhood whose closure is compact.
-
D.
typeOfCompactness
Indicates the specific kind or category of compactness that characterizes an entity or structure.
-
E.
isShort
Indicates that one entity has a relatively small height, length, or duration compared to a standard or to other entities.
- 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_69c6884e15208190b9e91487eaafcf85 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6da1f5fcc8190b43f53f90fc1821c |
completed | March 27, 2026, 7:27 p.m. |
| PD | Predicate disambiguation | batch_69c6d7bb577c81908ee8b415b4281f3d |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:27 p.m.