Triple
T34560479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Vincent’s Hospital, St. Augustine, Florida, United States |
E887323
|
entity |
| Predicate | hasNotablePatientDeath |
P51826
|
FINISHED |
| Object | Marjorie Kinnan Rawlings |
E255065
|
NE 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: Marjorie Kinnan Rawlings | Statement: [St. Vincent’s Hospital, St. Augustine, Florida, United States, hasNotablePatientDeath, Marjorie Kinnan Rawlings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotablePatientDeath Context triple: [St. Vincent’s Hospital, St. Augustine, Florida, United States, hasNotablePatientDeath, Marjorie Kinnan Rawlings]
-
A.
hasSubjectDeathEvent
Indicates that an entity is associated with an event representing the death of its subject.
-
B.
deathOccurred
Indicates that a death event has taken place involving the specified entity or entities.
-
C.
containsDeathOf
chosen
Indicates that the subject includes, depicts, or involves the death of the referenced entity.
-
D.
hasCauseOfDeathInvestigation
Indicates that an entity is the subject of an official investigation into the circumstances or cause of its death.
-
E.
hasMannerOfDeath
Indicates the specific way or circumstances in which an entity died, such as natural causes, accident, homicide, or suicide.
- F. None of above.
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_69f349d0c4d881908dd0950f5eb9ec0a |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a032b2285ac81908826f311a222c749 |
completed | May 12, 2026, 1:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a373625f6a8819096548957860efd30 |
completed | June 21, 2026, 12:53 a.m. |
| PD | Predicate disambiguation | batch_6a032929b8b88190bf7d14d789b38aeb |
completed | May 12, 2026, 1:20 p.m. |
Created at: May 1, 2026, 2:02 a.m.