Triple
T33499783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | E.R. |
E857956
|
entity |
| Predicate | setInFictionalHospital |
P18263
|
FINISHED |
| Object | County General Hospital |
—
|
NE NERFINISHED |
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: County General Hospital | Statement: [E.R., setInFictionalHospital, County General Hospital]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: setInFictionalHospital Context triple: [E.R., setInFictionalHospital, County General Hospital]
-
A.
fictionalHospital
Indicates that a hospital is imaginary or exists only within a fictional or narrative context, rather than in reality.
-
B.
setInFictionalLocation
chosen
Indicates that an event, story, or narrative takes place within a fictional or imagined location rather than a real-world setting.
-
C.
hasFictionalClinic
Indicates that an entity is associated with or contains a clinic that exists only in a fictional or imaginary context.
-
D.
worksAtHospital
Indicates that a person is employed at and performs their professional duties in a hospital.
-
E.
setInFictionalOrganization
Indicates that an entity is located within, associated with, or takes place inside a fictional organization.
- 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_69f3497660508190a541826a81f7e9ab |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6fb19063c81909466b329655c8583 |
completed | May 3, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f6f96badb08190994442c2aba840b1 |
completed | May 3, 2026, 7:29 a.m. |
Created at: May 1, 2026, 1:38 a.m.