Triple
T25216779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vendôme station |
E631849
|
entity |
| Predicate | connectsToHospital |
P167044
|
FINISHED |
| Object | McGill University Health Centre |
—
|
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: McGill University Health Centre | Statement: [Vendôme station, connectsToHospital, McGill University Health Centre]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsToHospital Context triple: [Vendôme station, connectsToHospital, McGill University Health Centre]
-
A.
containsHospital
Indicates that one entity includes or encompasses a hospital within its boundaries or composition.
-
B.
hospitalizedIn
Indicates that a person or patient is admitted for medical care and staying as an inpatient in a specified hospital or healthcare facility.
-
C.
hasHospitalType
Indicates that a hospital is classified as belonging to a specific type or category (e.g., general, specialized, teaching).
-
D.
hasEmergencyCare
Indicates that an entity provides or is equipped with emergency medical care services for another entity or individuals.
-
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_69e75a8d1aa48190a4320acd3654762c |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f6653ccf648190b65fb1141928e47e |
completed | May 2, 2026, 8:57 p.m. |
| PD | Predicate disambiguation | batch_69f6633451948190bcc0410602bb4914 |
completed | May 2, 2026, 8:48 p.m. |
| PDg | Predicate description generation | batch_69f663ff176c8190aaadb475f75daee4 |
completed | May 2, 2026, 8:52 p.m. |
Created at: April 21, 2026, 12:59 p.m.