Triple
T7290364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TI-73 Explorer |
E164375
|
entity |
| Predicate | supportsMathematicsTopic |
P75416
|
FINISHED |
| Object | pre-algebra |
—
|
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: pre-algebra | Statement: [TI-73 Explorer, supportsMathematicsTopic, pre-algebra]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsMathematicsTopic Context triple: [TI-73 Explorer, supportsMathematicsTopic, pre-algebra]
-
A.
mathematicallyUses
Indicates that one entity employs or applies another entity within a mathematical context, such as in a formula, proof, computation, or theoretical framework.
-
B.
associatedMatha
Indicates a relationship where one entity is linked or affiliated with a particular matha (monastic or religious institution).
-
C.
hasMatha
Indicates a relationship where one entity possesses, is associated with, or is characterized by a specific matha (monastic institution or religious seat).
-
D.
mathematicalSubjectClassification
Indicates that one entity classifies the mathematical subject area or field to which another entity (such as a work, concept, or topic) belongs.
-
E.
supportsEquations
Indicates that one entity provides the capability to handle, display, or work with mathematical equations for another entity or within a given context.
- 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_69c6887a499881909dd23341399c59d8 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6eb6d4e308190af6b8c237988d7d8 |
completed | March 27, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69c6e76c5fbc8190b378830082f11cb0 |
completed | March 27, 2026, 8:24 p.m. |
| PDg | Predicate description generation | batch_69c6e82b0f9881909d29c99af1ea0dbf |
completed | March 27, 2026, 8:27 p.m. |
Created at: March 27, 2026, 3 p.m.