Triple
T4310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hospital of the University of Pennsylvania |
E83
|
entity |
| Predicate | hasEmergencyServices |
P464
|
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: [Hospital of the University of Pennsylvania, hasEmergencyServices, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmergencyServices Context triple: [Hospital of the University of Pennsylvania, hasEmergencyServices, yes]
-
A.
hasNotableFacility
Indicates that an entity possesses or hosts a facility that is of particular significance, prominence, or interest.
-
B.
hasAreaCode
Indicates that a specified telephone area code is assigned to or associated with a particular geographic region, location, or phone service entity.
-
C.
hasMilitaryBranch
Indicates that an entity is associated with, served in, or is part of a specific branch of a military organization.
-
D.
areaServed
Indicates the geographic region or jurisdiction within which a service, organization, or activity is provided or applicable.
-
E.
hasMajorCity
Indicates that a location possesses at least one city of significant size, importance, or influence within its region or country.
- 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.