Triple
T25665759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caroline Todd |
E643510
|
entity |
| Predicate | fictionalEmployer |
P109189
|
FINISHED |
| Object | East Hampton Hospital Trust |
—
|
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: East Hampton Hospital Trust | Statement: [Caroline Todd, fictionalEmployer, East Hampton Hospital Trust]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalEmployer Context triple: [Caroline Todd, fictionalEmployer, East Hampton Hospital Trust]
-
A.
fictionalCorporation
Indicates that an entity is a corporation that exists only in fiction rather than in the real world.
-
B.
fictionalOrganizationFeatured
Indicates that a fictional organization is prominently presented or plays a significant role within a given work or context.
-
C.
worksForFictionalOrganization
chosen
Indicates that an entity is employed by or affiliated as a worker with a fictional organization.
-
D.
fictionalOrganizationName
Indicates that the relationship specifies the name assigned to a fictional organization.
-
E.
hasFictionalCorporation
Indicates that an entity is associated with or includes a fictional corporation within its content, setting, or narrative.
- 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_69e77e7e45648190a068ed3faa8016ea |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651a731508190bb0c8c2462eba224 |
completed | May 2, 2026, 7:33 p.m. |
Created at: April 21, 2026, 7:04 p.m.