Triple
T1382301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | method of least squares |
E29364
|
entity |
| Predicate | optimizationType |
P27179
|
FINISHED |
| Object | unconstrained optimization |
—
|
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: unconstrained optimization | Statement: [method of least squares, optimizationType, unconstrained optimization]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: optimizationType Context triple: [method of least squares, optimizationType, unconstrained optimization]
-
A.
approximationType
Indicates the specific method or scheme used to approximate a value, function, or relationship in a given context.
-
B.
usesOpticsType
Indicates that one entity employs or is characterized by a specific type of optical system or technology.
-
C.
calculationType
Indicates the specific method, formula, or approach used to perform a calculation in the described relationship.
-
D.
adaptationType
Indicates the specific kind or category of adaptation that relates one entity to another or to a particular context.
-
E.
typeOfOperation
Indicates the specific kind or category of operation being performed or referenced in a given context.
- 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_69a498d883a48190bfdca525296ef7ee |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c3361bf08190b3f6bbf82e17685b |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4befe343c81909f758440a531b5be |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c0335f7081908d50046ced4cdee0 |
completed | March 1, 2026, 10:39 p.m. |
Created at: March 1, 2026, 7:59 p.m.