Triple
T7338648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Farey tessellation |
E169192
|
entity |
| Predicate | hasVertexSet |
P77195
|
FINISHED |
| Object | extended rational numbers |
—
|
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: extended rational numbers | Statement: [Farey tessellation, hasVertexSet, extended rational numbers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVertexSet Context triple: [Farey tessellation, hasVertexSet, extended rational numbers]
-
A.
hasVertex
Indicates that an object, typically a geometric shape or graph, includes a specified vertex as one of its corner or node points.
-
B.
hasDistinctSetFor
Indicates that one entity is associated with a separate, non-overlapping collection of items or elements specifically designated for another entity.
-
C.
hasVector
Indicates that an entity is associated with, or can be represented by, a specific vector in some vector space.
-
D.
hasLabelSet
Indicates that an entity is associated with a specific collection or set of labels.
-
E.
hasNumberOfPoints
Indicates that an entity is associated with a specific count of points it possesses or comprises.
- 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_69c68a57710481909f0c1f3c6ebdb6f2 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f347f25081908e6086d4073295f5 |
completed | March 27, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69c6f028fd748190b2ea5c3081958a42 |
completed | March 27, 2026, 9:01 p.m. |
| PDg | Predicate description generation | batch_69c6f3463d0481908aed9ed43a8ac6a8 |
completed | March 27, 2026, 9:14 p.m. |
Created at: March 27, 2026, 3:04 p.m.