Triple
T28782679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PECASE |
E726715
|
entity |
| Predicate | typicalAwardeesPerYear |
P1620
|
FINISHED |
| Object | dozens |
—
|
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: dozens | Statement: [PECASE, typicalAwardeesPerYear, dozens]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAwardeesPerYear Context triple: [PECASE, typicalAwardeesPerYear, dozens]
-
A.
typicalNumberOfLaureatesPerYear
chosen
Indicates the usual or average number of laureates associated with a given award or context in a single year.
-
B.
typicalNumberOfLaureatesPerCycle
Indicates the usual or average number of laureates associated with each award cycle or iteration.
-
C.
numberOfAwardsPerYear
Indicates the number of awards associated with an entity within a given year.
-
D.
typicalAwardYearPattern
Indicates the usual or standard pattern in which award years are assigned or distributed for a given award.
-
E.
maximumNumberOfLaureatesPerYear
Indicates the highest allowable or observed count of laureates associated with a given 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_69f0319aabec81908368720196f69a35 |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_6a01203ac018819085d7c9abd8345b23 |
completed | May 11, 2026, 12:18 a.m. |
| PD | Predicate disambiguation | batch_6a011f3f149c819090749722da0e3413 |
completed | May 11, 2026, 12:13 a.m. |
Created at: April 28, 2026, 6:20 a.m.