Triple
T25609821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2020 Alexei Navalny poisoning |
E642016
|
entity |
| Predicate | locationHospitalizationInitial |
P22425
|
FINISHED |
| Object | Omsk Emergency Hospital No. 1 |
—
|
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: Omsk Emergency Hospital No. 1 | Statement: [2020 Alexei Navalny poisoning, locationHospitalizationInitial, Omsk Emergency Hospital No. 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locationHospitalizationInitial Context triple: [2020 Alexei Navalny poisoning, locationHospitalizationInitial, Omsk Emergency Hospital No. 1]
-
A.
hospitalizedIn
chosen
Indicates that a person or patient is admitted for medical care and staying as an inpatient in a specified hospital or healthcare facility.
-
B.
containsHospital
Indicates that one entity includes or encompasses a hospital within its boundaries or composition.
-
C.
hospitalizedFor
Indicates that an entity is admitted to and staying in a medical facility specifically due to a particular illness, condition, or medical reason.
-
D.
hospitalLocation
Indicates the geographic place or address where a hospital is situated.
-
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_69e75dc6ccf081908d49578fd36a76d5 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f78c61ed4c8190ad84c918fa9af55a |
completed | May 3, 2026, 5:56 p.m. |
| PD | Predicate disambiguation | batch_69f78b8cb3a881909ebaac1b503988c2 |
completed | May 3, 2026, 5:53 p.m. |
Created at: April 21, 2026, 4:40 p.m.