Triple
T6800986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lagrange's four-square theorem |
E156185
|
entity |
| Predicate | minimalNumberOfSquaresGuaranteed |
P26954
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Lagrange's four-square theorem, minimalNumberOfSquaresGuaranteed, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: minimalNumberOfSquaresGuaranteed Context triple: [Lagrange's four-square theorem, minimalNumberOfSquaresGuaranteed, 4]
-
A.
hasNumberOfSquares
Indicates that an entity is associated with a specific count of squares it contains or comprises.
-
B.
minimumCircuitsRequired
Indicates the smallest number of circuits that must be present or used for a given system, configuration, or requirement to be satisfied.
-
C.
minimumNumber
chosen
Indicates that the associated value is the smallest or least quantity allowed, required, or observed within a given set or context.
-
D.
maximumNumber
Indicates that one entity specifies the highest allowable or observed quantity, value, or count associated with another entity.
-
E.
minimumNumberOfGames
Indicates the smallest required count of games that must be played or satisfied in a given context or constraint.
- 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_69c68826e6a48190a3d220b541e639de |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2e595188190a0bb4b595df3adb2 |
completed | March 27, 2026, 6:56 p.m. |
| PD | Predicate disambiguation | batch_69c6d099bf08819089a9f9894d037e74 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:16 p.m.