Triple
T8019341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regeneron Science Talent Search finalists |
E186698
|
entity |
| Predicate | typicalCountPerYear |
P17050
|
FINISHED |
| Object | 40 |
—
|
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: 40 | Statement: [Regeneron Science Talent Search finalists, typicalCountPerYear, 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCountPerYear Context triple: [Regeneron Science Talent Search finalists, typicalCountPerYear, 40]
-
A.
typicalNumberOfRecipientsPerYear
chosen
Indicates the usual or average count of recipients involved in or affected by something within a one-year period.
-
B.
typicalMonthOfOccurrence
Indicates the month in which something most commonly or typically occurs.
-
C.
typicalIn
Indicates that something commonly occurs, appears, or is found within a given context, category, or environment.
-
D.
totalCommonYears
Indicates the total number of years that two or more entities have in common, such as overlapping durations or shared time periods.
-
E.
touristArrivalsPerYearApprox
Indicates an approximate count of how many tourists arrive at a place over the course of a year.
- 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_69ca82ac7fc081909b1398cf025423af |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3e8bc90081909f6f5878e6f1f241 |
completed | March 31, 2026, 3:24 a.m. |
| PD | Predicate disambiguation | batch_69cb049253d08190bafcecfde493ab8b |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:20 p.m.