Triple
T35678456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Francis Hospital |
E1030930
|
entity |
| Predicate | hasSuccessorFacilityType |
P201368
|
FINISHED |
| Object | regional medical center |
—
|
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: regional medical center | Statement: [St. Francis Hospital, hasSuccessorFacilityType, regional medical center]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSuccessorFacilityType Context triple: [St. Francis Hospital, hasSuccessorFacilityType, regional medical center]
-
A.
successorFacility
Indicates that one facility directly follows and replaces another in function, ownership, or operation.
-
B.
successorFacilityLocation
Indicates that one facility location directly follows or replaces another in a sequence or succession of locations.
-
C.
successorType
Indicates the specific kind or category of successor relationship that holds between one entity and the next in a sequence or hierarchy.
-
D.
hasSuccessorShip
Indicates that one entity is the ship that follows or replaces another ship in a sequence or lineage.
-
E.
hasCitySuccessor
Indicates that one city is the successor or replacement of another city, typically in terms of status, function, or administrative role.
- 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_69f76e0bb6608190ad3a1880be54a17d |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ffed8912488190baa05f572e5b1b89 |
completed | May 10, 2026, 2:29 a.m. |
| PD | Predicate disambiguation | batch_69ffed12a76c8190ad85c6ac869c72e9 |
completed | May 10, 2026, 2:27 a.m. |
| PDg | Predicate description generation | batch_69ffed884b908190b12422c790b1525e |
completed | May 10, 2026, 2:29 a.m. |
Created at: May 3, 2026, 4:05 p.m.