Triple
T37883759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | First Clinical College |
E944941
|
entity |
| Predicate | associatedMedicalSchool |
P13371
|
FINISHED |
| Object | Tongji Medical College |
E279428
|
NE 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: Tongji Medical College | Statement: [First Clinical College, associatedMedicalSchool, Tongji Medical College]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedMedicalSchool Context triple: [First Clinical College, associatedMedicalSchool, Tongji Medical College]
-
A.
medicalSchool
Indicates that one entity serves as the medical school where the other entity received medical education or training.
-
B.
hasMedicalCollege
chosen
Indicates that one entity possesses, hosts, or includes a medical college as part of its organization or structure.
-
C.
foundedAsMedicalSchoolOf
Indicates that an institution was originally established as the medical school component of another institution.
-
D.
coordinatesMedicalEducation
Indicates that one entity organizes, manages, or oversees the medical education activities or programs involving another entity.
-
E.
associatedInstitution
Indicates that an entity has a formal connection or affiliation with a particular institution.
- F. None of above.
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_69f76ef02668819089e7940c4001af5e |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41cc8d5cd88190af33a4e61b4d8e9f |
completed | June 29, 2026, 1:38 a.m. |
| PD | Predicate disambiguation | batch_6a037a192a008190a9917688a9e804f4 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:19 p.m.