Triple
T26279259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | assassination of Martin Luther King Jr. |
E660658
|
entity |
| Predicate | followedByMedicalTreatmentAt |
P169912
|
FINISHED |
| Object | St. Joseph's Hospital, Memphis |
—
|
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: St. Joseph's Hospital, Memphis | Statement: [assassination of Martin Luther King Jr., followedByMedicalTreatmentAt, St. Joseph's Hospital, Memphis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: followedByMedicalTreatmentAt Context triple: [assassination of Martin Luther King Jr., followedByMedicalTreatmentAt, St. Joseph's Hospital, Memphis]
-
A.
hasReceivedTreatmentFor
Indicates that an entity has undergone or been given a treatment in relation to a specified condition, issue, or problem.
-
B.
hasSubsequentTreatment
Indicates that one treatment occurs after and in continuation of another treatment in a temporal sequence.
-
C.
subsequentTreatment
Indicates that one treatment occurs after and in response to a prior treatment or medical event.
-
D.
usesTreatment
Indicates that one entity applies or employs a particular treatment or therapeutic method on or for another entity.
-
E.
hasMedicalAttendant
Indicates that one entity serves as a medical attendant (e.g., providing medical care or supervision) for another entity.
- 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_69ee812960d081909cff6085cc9fa3a6 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f688d015908190ad5df37030ecf332 |
completed | May 2, 2026, 11:29 p.m. |
| PD | Predicate disambiguation | batch_69f68609c0b08190a8e1238a4d97c270 |
completed | May 2, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69f688034580819086a0f9100645f8ba |
completed | May 2, 2026, 11:25 p.m. |
Created at: April 26, 2026, 9:58 p.m.