Triple
T27088961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zola Grey Shepherd |
E686107
|
entity |
| Predicate | associatedHospitalInStory |
P20607
|
FINISHED |
| Object | Grey Sloan Memorial 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: Grey Sloan Memorial Hospital | Statement: [Zola Grey Shepherd, associatedHospitalInStory, Grey Sloan Memorial Hospital]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedHospitalInStory Context triple: [Zola Grey Shepherd, associatedHospitalInStory, Grey Sloan Memorial Hospital]
-
A.
hasAffiliatedHospital
chosen
Indicates that one entity (typically a medical professional, clinic, or organization) is formally connected or associated with a particular hospital for professional or operational purposes.
-
B.
fictionalHospital
Indicates that a hospital is imaginary or exists only within a fictional or narrative context, rather than in reality.
-
C.
containsHospital
Indicates that one entity includes or encompasses a hospital within its boundaries or composition.
-
D.
hospitalLocation
Indicates the geographic place or address where a hospital is situated.
-
E.
hospitalizedIn
Indicates that a person or patient is admitted for medical care and staying as an inpatient in a specified hospital or healthcare 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_69ef148940ec819097b5c20fbfbf7c81 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69ffb5c373948190a6606e8caa87a384 |
completed | May 9, 2026, 10:31 p.m. |
| PD | Predicate disambiguation | batch_69ffb261da788190b41399df8ed895e8 |
completed | May 9, 2026, 10:17 p.m. |
Created at: April 27, 2026, 8:39 a.m.