Triple
T1594066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Tokyo Hospital |
E34238
|
entity |
| Predicate | hasInpatientServices |
P10262
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [University of Tokyo Hospital, hasInpatientServices, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInpatientServices Context triple: [University of Tokyo Hospital, hasInpatientServices, yes]
-
A.
hospitalizedIn
Indicates that a person or patient is admitted for medical care and staying as an inpatient in a specified hospital or healthcare facility.
-
B.
hasDischarge
Indicates that one entity releases, emits, or expels a substance, energy, or flow from itself.
-
C.
isPublicHospital
Indicates that a hospital is owned, funded, or operated by a government or public authority rather than by private entities.
-
D.
hasPatient
Indicates that an action, event, or process involves a specific entity as the one undergoing or receiving its effects (the patient).
-
E.
hasMedicalCenter
chosen
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
- 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_69a885fdcb9c819081ce6f0b8cd477dd |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a916d413f08190a4e137e5ed262e25 |
completed | March 5, 2026, 5:38 a.m. |
| PD | Predicate disambiguation | batch_69a907bfb39c8190a31e0be14d3d52e6 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:27 p.m.