Triple
T17676535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SymTridiagonal |
E440654
|
entity |
| Predicate | diagonalCount |
P128519
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [SymTridiagonal, diagonalCount, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: diagonalCount Context triple: [SymTridiagonal, diagonalCount, 3]
-
A.
diagonalProperty
Indicates a relationship where something possesses or exhibits a diagonal characteristic, alignment, or behavior relative to a reference frame or structure.
-
B.
diagonalRepresents
Indicates that a diagonal in a figure or matrix stands for, encodes, or symbolizes a particular value, property, or relationship.
-
C.
hasNumberOfCrosses
Indicates the quantity of crosses associated with or present on a given entity.
-
D.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
E.
numberOfColumnsOnFlanks
Indicates the count of columns located on the flanking sides of a structure or object.
- 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46f6d9ab88190ab0e25eac8b0101c |
completed | April 19, 2026, 6 a.m. |
| PD | Predicate disambiguation | batch_69e3cde007d8819090dd92eea9f022cc |
completed | April 18, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69e3cfaac2b881909e1140339eb1a0dd |
completed | April 18, 2026, 6:38 p.m. |
Created at: April 10, 2026, 10:01 a.m.