Triple
T738046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | von Neumann universe |
E14977
|
entity |
| Predicate | V_0Equals |
P7398
|
FINISHED |
| Object | empty 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: empty set | Statement: [von Neumann universe, V_0Equals, empty set]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: V_0Equals Context triple: [von Neumann universe, V_0Equals, empty set]
-
A.
isZeroFor
Indicates that a given value, expression, or function evaluates to zero when applied to or considered with respect to a specified entity or context.
-
B.
hasEquivalent
chosen
Indicates that two entities are considered equal in value, meaning, or function within a given context.
-
C.
equivalentTo
Indicates that two entities represent the same concept, value, or state, and can be treated as interchangeable in the given context.
-
D.
value
Indicates that one entity possesses, represents, or corresponds to a particular quantity, quality, or assigned worth.
-
E.
hasValley
Indicates that one entity contains, features, or is characterized by the presence of a valley associated with the other 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_69a4934d9930819099eed80096b0597d |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a64adf2c81908e48090be35dd9d9 |
completed | March 1, 2026, 8:49 p.m. |
| PD | Predicate disambiguation | batch_69a4a4fc734c81908fbd36386d5746d6 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.