Triple
T19454055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hawkins General Hospital |
E486689
|
entity |
| Predicate | hasFictionalEmergencyDepartment |
P120607
|
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: [Hawkins General Hospital, hasFictionalEmergencyDepartment, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalEmergencyDepartment Context triple: [Hawkins General Hospital, hasFictionalEmergencyDepartment, yes]
-
A.
hasFictionalEmergencyRoom
chosen
Indicates that an entity includes or features a fictional emergency room as part of its setting or content.
-
B.
hasFictionalClinic
Indicates that an entity is associated with or contains a clinic that exists only in a fictional or imaginary context.
-
C.
fictionalHospital
Indicates that a hospital is imaginary or exists only within a fictional or narrative context, rather than in reality.
-
D.
hasEmergencyDepartmentLevel
Indicates the specific classification or tier of emergency care capability associated with an emergency department.
-
E.
hasFictionalMediaOutlet
Indicates that an entity is associated with or features a fictional media outlet (such as an invented TV station, newspaper, or network) within its narrative or context.
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633c117ac8190a38c01c3191beaea |
completed | April 20, 2026, 2:10 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7499a4819082bec0be8afba35c |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:38 p.m.