Triple
T28572067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | murder of William McKinley |
E723138
|
entity |
| Predicate | hasMedicalTreatmentLocation |
P8558
|
FINISHED |
| Object | Buffalo, New York |
—
|
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: Buffalo, New York | Statement: [murder of William McKinley, hasMedicalTreatmentLocation, Buffalo, New York]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMedicalTreatmentLocation Context triple: [murder of William McKinley, hasMedicalTreatmentLocation, Buffalo, New York]
-
A.
treatmentLocation
chosen
Indicates the place or facility where a treatment or medical intervention is administered to an entity.
-
B.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
-
C.
hospitalLocation
Indicates the geographic place or address where a hospital is situated.
-
D.
hasMedicalUnit
Indicates that an entity possesses, includes, or is associated with a medical unit (such as a clinic, department, or medical team) as part of its structure or resources.
-
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_69f01d7e97708190ae9e77ee66a68abd |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69fcd867f36081908c88c55a6a1404c1 |
completed | May 7, 2026, 6:22 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f47b188190b4cf4b4c748d9d03 |
completed | May 7, 2026, 5:55 p.m. |
Created at: April 28, 2026, 4:10 a.m.