Triple
T17498212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Chung |
E426123
|
entity |
| Predicate | medicalDegree |
P127681
|
FINISHED |
| Object | M.D. |
—
|
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: M.D. | Statement: [Margaret Chung, medicalDegree, M.D.]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: medicalDegree Context triple: [Margaret Chung, medicalDegree, M.D.]
-
A.
medicalQualificationFrom
Indicates that a person or medical professional obtained their medical qualification or degree from a specified institution or source.
-
B.
medicalSchool
Indicates that one entity serves as the medical school where the other entity received medical education or training.
-
C.
medicalPractice
Indicates a relationship where an entity engages in or carries out the professional provision of medical care or services.
-
D.
coordinatesMedicalEducation
Indicates that one entity organizes, manages, or oversees the medical education activities or programs involving another entity.
-
E.
hasMedicalCollege
Indicates that one entity possesses, hosts, or includes a medical college as part of its organization or structure.
- 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_69d889dccf7481909264a1844a2e9100 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4520f6790819092c36e0e4ecc4cd3 |
completed | April 19, 2026, 3:54 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f5fbcc8190a6ea9639bf5650da |
completed | April 18, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69e3bbb37d148190b7f38599c06594ee |
completed | April 18, 2026, 5:13 p.m. |
Created at: April 10, 2026, 5:48 a.m.