Triple
T6929654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cantor set |
E160400
|
entity |
| Predicate | hasLebesgueMeasure |
P73727
|
FINISHED |
| Object | 0 |
—
|
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: 0 | Statement: [Cantor set, hasLebesgueMeasure, 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLebesgueMeasure Context triple: [Cantor set, hasLebesgueMeasure, 0]
-
A.
measurability
Indicates that a quantity, property, or outcome can be defined, quantified, or assessed using a consistent measurement framework.
-
B.
hasMeasurement
Indicates that an entity is associated with a specific measured value, often including a unit or measurement context.
-
C.
hasHyperbolicArea
Indicates that one entity possesses or is associated with a specific area measured in hyperbolic geometry.
-
D.
includesMeasure
Indicates that one entity contains, incorporates, or makes use of a particular measurement or quantitative value as part of its definition or specification.
-
E.
measuredAs
Indicates that one entity’s quantity, value, or property is expressed or recorded using another entity as its measurement or unit.
- 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_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. |
| PDg | Predicate description generation | batch_69c6d98625c88190a37fdf6d95d7fcbd |
completed | March 27, 2026, 7:24 p.m. |
Created at: March 27, 2026, 2:27 p.m.