Triple
T21821805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Master of Public Health |
E538744
|
entity |
| Predicate | commonConcentration |
P145806
|
FINISHED |
| Object | epidemiology |
—
|
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: epidemiology | Statement: [Master of Public Health, commonConcentration, epidemiology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonConcentration Context triple: [Master of Public Health, commonConcentration, epidemiology]
-
A.
concentration
Indicates the degree to which a substance or entity is present within a given medium, mixture, or space.
-
B.
concentrationClass
Indicates the classification of an entity based on the level or range of its concentration.
-
C.
commonCompound
Indicates that the two entities share at least one chemical compound in common.
-
D.
commonLevel
Indicates that two or more entities share the same level, rank, or hierarchical position within a given system or structure.
-
E.
commonIon
Indicates that two substances share at least one identical ion in solution, linking them through the common ion effect.
- 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_69e0c475038c8190abb9b1a20eb8ff50 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f0912e432481909045d00a61daa767 |
completed | April 28, 2026, 10:51 a.m. |
| PD | Predicate disambiguation | batch_69e6be815a108190be81d7c987d0c0d6 |
completed | April 21, 2026, 12:02 a.m. |
| PDg | Predicate description generation | batch_69e6c670ee608190b9cfdc09de74f0de |
completed | April 21, 2026, 12:36 a.m. |
Created at: April 16, 2026, 6:54 p.m.