Triple
T4312
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hospital of the University of Pennsylvania |
E83
|
entity |
| Predicate | hasSpecialty |
P466
|
FINISHED |
| Object | cardiology |
—
|
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: cardiology | Statement: [Hospital of the University of Pennsylvania, hasSpecialty, cardiology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpecialty Context triple: [Hospital of the University of Pennsylvania, hasSpecialty, cardiology]
-
A.
hasFaculty
Indicates that an institution or department possesses or is associated with one or more faculty members.
-
B.
hasPrimaryFunction
Indicates that one entity serves as the main or principal function or role of another entity.
-
C.
hasNotableFacility
Indicates that an entity possesses or hosts a facility that is of particular significance, prominence, or interest.
-
D.
hasAcademicRank
Indicates that an entity holds a specific academic rank or title within an educational or research institution.
-
E.
supportsDiscipline
Indicates that one entity provides assistance, resources, or endorsement that helps sustain or advance a particular discipline.
- 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_69a238d6b47881909e68288aed2fd858 |
completed | Feb. 28, 2026, 12:37 a.m. |
| NER | Named-entity recognition | batch_69a23c24b3d08190a714126292fd5479 |
completed | Feb. 28, 2026, 12:51 a.m. |
| PD | Predicate disambiguation | batch_69a23998af288190855f0456740cbd51 |
completed | Feb. 28, 2026, 12:40 a.m. |
| PDg | Predicate description generation | batch_69a23c23fef88190ba5d6d86acd4a66f |
completed | Feb. 28, 2026, 12:51 a.m. |
Created at: Feb. 28, 2026, 12:40 a.m.