Triple
T31638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nash embedding theorem |
E631
|
entity |
| Predicate | relatesConcept |
P463
|
FINISHED |
| Object | metric tensor |
—
|
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: metric tensor | Statement: [Nash embedding theorem, relatesConcept, metric tensor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatesConcept Context triple: [Nash embedding theorem, relatesConcept, metric tensor]
-
A.
hasConcept
Indicates that an entity includes, embodies, or is associated with a particular concept.
-
B.
introducedConcept
Indicates that one entity is responsible for presenting, defining, or bringing a new concept into use or awareness for another entity or context.
-
C.
relatedTo
Indicates a general, non-specific relationship or association exists between two entities.
-
D.
notablyAssociatedWith
Indicates that one entity is prominently or distinctively connected with another in a way that is especially noteworthy or remarkable.
-
E.
demonstratedConcept
chosen
Indicates that an entity has shown, illustrated, or made evident a particular concept through example, explanation, or action.
- 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_69a2479dec388190967ba648663442c9 |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a249ec0d288190ac3a0939db61813b |
completed | Feb. 28, 2026, 1:50 a.m. |
| PD | Predicate disambiguation | batch_69a24870417081909c7c01e400c94716 |
completed | Feb. 28, 2026, 1:44 a.m. |
Created at: Feb. 28, 2026, 1:44 a.m.