Triple
T17341086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Banach limit |
E421066
|
entity |
| Predicate | specialCaseExample |
P7025
|
FINISHED |
| Object | for convergent x_n, L(x)=lim x_n |
—
|
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: for convergent x_n, L(x)=lim x_n | Statement: [Banach limit, specialCaseExample, for convergent x_n, L(x)=lim x_n]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: specialCaseExample Context triple: [Banach limit, specialCaseExample, for convergent x_n, L(x)=lim x_n]
-
A.
specialCaseOf
chosen
Indicates that one entity represents a more specific, exceptional, or restricted instance of the general situation, rule, or relationship expressed by another entity.
-
B.
specialValue
Indicates that an entity possesses a distinguished or exceptional value compared to typical or default values in the given context.
-
C.
specialAppearance
Indicates that an entity makes a notable or exceptional appearance distinct from its usual or regular presence.
-
D.
standardExample
Indicates that something is a typical or canonical instance used to illustrate a general case or concept.
-
E.
majorExample
Indicates that one entity serves as a primary or most significant example or instance of another entity.
- 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_69d889d3adc881909319f1edb8d2a956 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a15f6488190ad7d489e7391ab12 |
completed | April 19, 2026, 2:12 a.m. |
| PD | Predicate disambiguation | batch_69e3b021a5bc81909ae55406f9d0b37f |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:44 a.m.