Triple
T15974263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nobel Prize in Physiology or Medicine 1979 |
E387403
|
entity |
| Predicate | laureateCount |
P1620
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Nobel Prize in Physiology or Medicine 1979, laureateCount, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laureateCount Context triple: [Nobel Prize in Physiology or Medicine 1979, laureateCount, 2]
-
A.
hasLaureate
Indicates that an entity (such as an award or prize) has a specific person or group as its laureate or recipient.
-
B.
typicalNumberOfLaureatesPerYear
chosen
Indicates the usual or average number of laureates associated with a given award or context in a single year.
-
C.
numberOfNobelPrizes
Indicates the count of Nobel Prizes that have been awarded to a given entity.
-
D.
typicalNumberOfLaureatesPerCycle
Indicates the usual or average number of laureates associated with each award cycle or iteration.
-
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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142d6fb588190b4176eab4bbae774 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:54 a.m.