Triple
T21046837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sperner family |
E518470
|
entity |
| Predicate | countingProblem |
P142613
|
FINISHED |
| Object | enumerate number of Sperner families on an n-element set |
—
|
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: enumerate number of Sperner families on an n-element set | Statement: [Sperner family, countingProblem, enumerate number of Sperner families on an n-element set]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countingProblem Context triple: [Sperner family, countingProblem, enumerate number of Sperner families on an n-element set]
-
A.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
B.
countingRule
Indicates the rule or method used to count or quantify items, events, or entities in a given context.
-
C.
alternativeCounting
Indicates that there exists another valid way of counting or enumerating the same set of items or events, distinct from the primary counting method.
-
D.
count
Indicates the numerical quantity or total number of instances of a specified entity or event.
-
E.
numberOfProblems
Indicates the quantity or count of problems associated with a given entity or situation.
- 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_69e0b50438e08190917e2538bb8bc034 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fcf4d26481908b639996500a8319 |
completed | April 21, 2026, 4:28 a.m. |
| PD | Predicate disambiguation | batch_69e5dbf6728881908a2a43a5c8804a2a |
completed | April 20, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69e5e2df1a888190b5b478e76bdf7fdf |
completed | April 20, 2026, 8:25 a.m. |
Created at: April 16, 2026, 2:34 p.m.