Triple
T5338250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hardy–Ramanujan asymptotic formula |
E123877
|
entity |
| Predicate | isLandmarkResultIn |
P63071
|
FINISHED |
| Object | partition theory |
—
|
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: partition theory | Statement: [Hardy–Ramanujan asymptotic formula, isLandmarkResultIn, partition theory]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isLandmarkResultIn Context triple: [Hardy–Ramanujan asymptotic formula, isLandmarkResultIn, partition theory]
-
A.
isLandmarkFor
Indicates that one entity serves as a notable or significant reference point or attraction for another entity, such as a place, route, or area.
-
B.
includesLandmark
Indicates that one location or area contains or encompasses a specific landmark within its boundaries.
-
C.
isLocalLandmark
Indicates that something is recognized as a notable or significant landmark within a specific local area or community.
-
D.
hasLandmarkArea
Indicates that a specified area is designated as the landmark area associated with a particular entity or location.
-
E.
resultForMonument
Indicates that something (such as data, analysis, or an outcome) is the result specifically associated with a given monument.
- 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_69bd464b07f8819095aa76577c9829e4 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85e86edc81908d87933db6489f91 |
completed | March 20, 2026, 5:37 p.m. |
| PD | Predicate disambiguation | batch_69bd845a62b081909782863865b257a9 |
completed | March 20, 2026, 5:31 p.m. |
| PDg | Predicate description generation | batch_69bd85e69e808190b29548670fd2900a |
completed | March 20, 2026, 5:37 p.m. |
Created at: March 20, 2026, 2 p.m.