Triple
T2075054
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zuckerberg San Francisco General Hospital and Trauma Center |
E44901
|
entity |
| Predicate | hasTeachingProgramsIn |
P2489
|
FINISHED |
| Object | medicine |
—
|
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: medicine | Statement: [Zuckerberg San Francisco General Hospital and Trauma Center, hasTeachingProgramsIn, medicine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTeachingProgramsIn Context triple: [Zuckerberg San Francisco General Hospital and Trauma Center, hasTeachingProgramsIn, medicine]
-
A.
hasDoctoralPrograms
Indicates that an institution offers one or more doctoral-level academic degree programs.
-
B.
hasEducationalProgram
chosen
Indicates that an entity offers, runs, or is associated with a specific educational program.
-
C.
hasUndergraduatePrograms
Indicates that an educational institution offers one or more undergraduate-level academic programs.
-
D.
hasDoctoralProgram
Indicates that an institution or academic unit offers and administers a doctoral-level degree program.
-
E.
grantsDegreesFrom
Indicates that an institution has the authority to confer academic degrees originating from a specified source or program.
- 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_69a88916c2b48190a5ca2e9b12cad3ed |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abba116ea0819086d16c3913159e9e |
completed | March 7, 2026, 5:39 a.m. |
| PD | Predicate disambiguation | batch_69abb7b0edac8190a58eabee55f73deb |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:41 p.m.