Triple
T34810988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | assassination of Robert F. Kennedy |
E1003495
|
entity |
| Predicate | medicalResponseLocation |
P8558
|
FINISHED |
| Object | Good Samaritan Hospital (Los Angeles) |
—
|
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: Good Samaritan Hospital (Los Angeles) | Statement: [assassination of Robert F. Kennedy, medicalResponseLocation, Good Samaritan Hospital (Los Angeles)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: medicalResponseLocation Context triple: [assassination of Robert F. Kennedy, medicalResponseLocation, Good Samaritan Hospital (Los Angeles)]
-
A.
hospitalLocation
Indicates the geographic place or address where a hospital is situated.
-
B.
treatmentLocation
chosen
Indicates the place or facility where a treatment or medical intervention is administered to an entity.
-
C.
encounterLocation
Indicates the place or setting where two or more entities meet, interact, or come into contact.
-
D.
apseLocation
Indicates the specific place or position where an apse is situated within a larger structure or context.
-
E.
placeOfReception
Indicates the location where something (such as a person, item, or message) is received or accepted.
- 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_69f76db600b88190989abdf08fce3b27 |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f77ab4a9dc8190ad41fb613f35ddad |
completed | May 3, 2026, 4:41 p.m. |
| PD | Predicate disambiguation | batch_69f7795b1abc8190823664d1caa94649 |
completed | May 3, 2026, 4:35 p.m. |
Created at: May 3, 2026, 3:59 p.m.