Triple
T469481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hôpital Sainte-Croix, Léogâne |
E8522
|
entity |
| Predicate | typeOfCare |
P7500
|
FINISHED |
| Object | medical care |
—
|
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: medical care | Statement: [Hôpital Sainte-Croix, Léogâne, typeOfCare, medical care]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfCare Context triple: [Hôpital Sainte-Croix, Léogâne, typeOfCare, medical care]
-
A.
healthcareType
chosen
Indicates the category or kind of healthcare service, system, or coverage associated with an entity.
-
B.
providesCareSetting
Indicates that one entity serves as the care environment or setting in which another entity receives or delivers care.
-
C.
typeOfSupport
Indicates the kind or category of assistance, help, or backing provided in a given context.
-
D.
typeOfAdministration
Indicates the specific form or system of administration applied to or governing an entity.
-
E.
diseaseType
Indicates that one entity is classified as a specific type or category of disease in relation to another entity.
- 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_69a2e7f3aeb48190a19453e3a043f486 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2efee0ea0819099d87f3727c03bc7 |
completed | Feb. 28, 2026, 1:38 p.m. |
| PD | Predicate disambiguation | batch_69a2edebb3988190907992a584b4e260 |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.