Triple
T12011674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Whitney sum |
E285917
|
entity |
| Predicate | preservesSmoothStructure |
P4235
|
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: [Whitney sum, preservesSmoothStructure, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: preservesSmoothStructure Context triple: [Whitney sum, preservesSmoothStructure, true]
-
A.
preservesFeature
Indicates that one entity maintains or keeps intact a particular feature, property, or characteristic of another entity during some process or interaction.
-
B.
invariantUnder
chosen
Indicates that a property, structure, or quantity remains unchanged when a specified transformation or operation is applied.
-
C.
preservesFragmentsOf
Indicates that one entity maintains, protects, or keeps intact partial remains or pieces of another entity.
-
D.
homeomorphicTo
Indicates that two topological spaces can be continuously deformed into each other via a bijective, continuous map with a continuous inverse, preserving their topological structure.
-
E.
somePreservedAs
Indicates that at least one part or instance of an entity is kept or maintained in a particular state, form, or condition.
- 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_69d6ab45a368819084fce08bf0dc3705 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903d7777481908cd5a001f75e2ee3 |
completed | April 10, 2026, 2:06 p.m. |
| PD | Predicate disambiguation | batch_69d902b245cc8190af96a9c2bd9c6250 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:46 p.m.