Triple
T14334535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramanujan–Nagell equation |
E355435
|
entity |
| Predicate | degreeInX |
P114012
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Ramanujan–Nagell equation, degreeInX, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: degreeInX Context triple: [Ramanujan–Nagell equation, degreeInX, 2]
-
A.
degreeNumber
Indicates the specific numeric value assigned to a degree, such as its level, rank, or sequence number.
-
B.
degreeForm
Indicates that one entity is the specific academic degree or qualification conferred in the context of another entity (such as a program, award, or credential).
-
C.
degreeOver
Indicates that one entity’s degree, level, or extent exceeds that of another entity.
-
D.
notionOfDegree
Indicates a relationship where one entity specifies or characterizes the degree, intensity, or extent to which a property or condition applies to another entity.
-
E.
hasNumberOfDegrees
Indicates the quantity of academic degrees that an entity possesses.
- 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_69d8278fa2108190bc0d0e7939c1eb03 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8c20d2148190bb534bef338e871d |
completed | April 14, 2026, 6:49 p.m. |
| PD | Predicate disambiguation | batch_69de2a9515f4819081aabf251bca5878 |
completed | April 14, 2026, 11:52 a.m. |
| PDg | Predicate description generation | batch_69de2e9ded24819099200349cf80e068 |
completed | April 14, 2026, 12:10 p.m. |
Created at: April 10, 2026, 1:13 a.m.