Triple
T14265339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tarski’s fixed point theorem |
E353628
|
entity |
| Predicate | typicalDomainExample |
P24492
|
FINISHED |
| Object | powerset lattice of a set |
—
|
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: powerset lattice of a set | Statement: [Tarski’s fixed point theorem, typicalDomainExample, powerset lattice of a set]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalDomainExample Context triple: [Tarski’s fixed point theorem, typicalDomainExample, powerset lattice of a set]
-
A.
typicalDomain
chosen
Indicates that one entity is the characteristic or most common domain, context, or area of application in which another entity typically occurs or is used.
-
B.
exampleDomain
Indicates a general or illustrative relationship used as a placeholder within a specific conceptual or application domain.
-
C.
standardDomain
Indicates that something belongs to, or is defined within, the usual or default domain of discourse or applicability for a given context.
-
D.
defaultDomain
Indicates that one entity serves as the standard or primary domain associated with another entity, used when no more specific domain is specified.
-
E.
publicDomain
Indicates that a work or resource is not protected by intellectual property rights and is freely available for anyone to use, copy, modify, and distribute without restriction.
- 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_69d8278c43e08190824146f4632b89a5 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6357a8188190ba518a486521052b |
completed | April 14, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69de2a7d586c8190846ff242bbf5ac53 |
completed | April 14, 2026, 11:52 a.m. |
Created at: April 10, 2026, 1:09 a.m.