Triple
T28880751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Starobinsky R^2 inflation model |
E732409
|
entity |
| Predicate | predictsTensorToScalarRatio |
P112259
|
FINISHED |
| Object | r ≈ 0.003 (for N ≈ 60 e-folds) |
—
|
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: r ≈ 0.003 (for N ≈ 60 e-folds) | Statement: [Starobinsky R^2 inflation model, predictsTensorToScalarRatio, r ≈ 0.003 (for N ≈ 60 e-folds)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: predictsTensorToScalarRatio Context triple: [Starobinsky R^2 inflation model, predictsTensorToScalarRatio, r ≈ 0.003 (for N ≈ 60 e-folds)]
-
A.
coreTensorProperty
Indicates that a tensor possesses a fundamental or defining property relevant to its core mathematical or structural characteristics.
-
B.
representationRatio
Indicates the proportional relationship between how much one entity represents, depicts, or stands in for another relative to some whole or reference amount.
-
C.
predictionType
Indicates the kind or category of prediction being made about an entity or event.
-
D.
predictionAlgorithm
Indicates a relationship where an algorithm generates predictions or forecasts about outcomes based on input data or observed patterns.
-
E.
predictionBy
chosen
Indicates that one entity serves as the source or author of a prediction made about another entity or outcome.
- 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_69f05b06807c81909b4bbd4c20403a2b |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f65a6d639481909e661755a5838a47 |
completed | May 2, 2026, 8:11 p.m. |
| PD | Predicate disambiguation | batch_69f65762b5e481908a30ca963dcba4be |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 28, 2026, 7:43 a.m.