Triple
T37196798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chow groups |
E921616
|
entity |
| Predicate | classify |
P188848
|
FINISHED |
| Object | algebraic cycles up to rational equivalence |
—
|
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: algebraic cycles up to rational equivalence | Statement: [Chow groups, classify, algebraic cycles up to rational equivalence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: classify Context triple: [Chow groups, classify, algebraic cycles up to rational equivalence]
-
A.
structureLearning
Indicates a process in which an agent infers or constructs the underlying structure or dependency relationships within a set of variables, data, or a model.
-
B.
structureNote
Indicates a note or annotation that describes or clarifies the structure, organization, or arrangement of something.
-
C.
approximability
Indicates that one entity can be closely estimated, represented, or approached in value, form, or behavior by another, typically within some defined margin of error.
-
D.
identifiability
Indicates that an entity or set of entities can be uniquely distinguished or determined based on the available information or observations.
-
E.
parameterLearning
Indicates a process or relationship in which parameters of a model, system, or function are adjusted or inferred—typically from data—to improve performance or fit.
- 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_69f76ea313a08190a54404cd1e47da90 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fbad1e94988190b86d447a68e65067 |
completed | May 6, 2026, 9:05 p.m. |
| PD | Predicate disambiguation | batch_69fba881b8e0819094790935152b99a1 |
completed | May 6, 2026, 8:45 p.m. |
| PDg | Predicate description generation | batch_69fbad1b3ba08190ad69e21461333f2e |
completed | May 6, 2026, 9:05 p.m. |
Created at: May 3, 2026, 4:15 p.m.