Triple
T32141729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sérsic index |
E820922
|
entity |
| Predicate | fittedUsing |
P113729
|
FINISHED |
| Object | nonlinear least-squares methods |
—
|
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: nonlinear least-squares methods | Statement: [Sérsic index, fittedUsing, nonlinear least-squares methods]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fittedUsing Context triple: [Sérsic index, fittedUsing, nonlinear least-squares methods]
-
A.
usedToCalibrate
Indicates that one entity serves as a reference or standard to adjust, tune, or verify the accuracy of another entity.
-
B.
calibratedFor
Indicates that something has been adjusted or tuned to operate accurately or optimally for a specific target, condition, or context.
-
C.
adoptedModel
Indicates that one entity has formally chosen, accepted, or implemented another entity as its preferred model or standard.
-
D.
modeledWith
chosen
Indicates that something is represented, simulated, or described using a particular model, method, or modeling technique.
-
E.
formulaUsed
Indicates that a particular formula is employed or applied in performing a calculation, derivation, or reasoning step.
- 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_69f3490520d081909b2f1271dab75faa |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b9aed8c881908214b59cb895fa65 |
completed | May 3, 2026, 2:57 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a970b0819090c6473844ffa8e3 |
completed | May 3, 2026, 2:32 a.m. |
Created at: May 1, 2026, 12:30 a.m.