Triple
T1248980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pascal's triangle |
E26830
|
entity |
| Predicate | nthRowRepresents |
P103
|
FINISHED |
| Object | coefficients of the binomial expansion of (x + y)^n |
—
|
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: coefficients of the binomial expansion of (x + y)^n | Statement: [Pascal's triangle, nthRowRepresents, coefficients of the binomial expansion of (x + y)^n]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nthRowRepresents Context triple: [Pascal's triangle, nthRowRepresents, coefficients of the binomial expansion of (x + y)^n]
-
A.
numberOfColumns
Indicates the total count of vertical divisions (columns) associated with or contained in a given structure or dataset.
-
B.
rows
Indicates that one entity is arranged in a horizontal line or sequence relative to another, typically as part of a grid or tabular structure.
-
C.
numberOfInnerColumns
Indicates the count of inner columns contained within or defined by a given structure or entity.
-
D.
ordinalNumber
Indicates the position or rank of an entity within an ordered sequence (e.g., first, second, third).
-
E.
hasRepresentationIn
chosen
Indicates that one entity is represented, depicted, or encoded within another entity, such as a concept, object, or data structure having a corresponding representation in a specific medium or context.
- 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_69a49487a9c48190ba9b05348fd1b53f |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf83b32c81908648e5748b897247 |
completed | March 1, 2026, 10:36 p.m. |
| PD | Predicate disambiguation | batch_69a4bb6b075881908e867c25b5080e25 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:47 p.m.