Triple
T23382137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hôtel-Dieu de Lyon |
E593775
|
entity |
| Predicate | medicalInstitutionUntil |
P152019
|
FINISHED |
| Object | 2010 |
—
|
LITERAL 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: 2010 | Statement: [Hôtel-Dieu de Lyon, medicalInstitutionUntil, 2010]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: medicalInstitutionUntil Context triple: [Hôtel-Dieu de Lyon, medicalInstitutionUntil, 2010]
-
A.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
-
B.
hospitalLocation
Indicates the geographic place or address where a hospital is situated.
-
C.
hospitalEstablishedIn
Indicates that a hospital was founded, opened, or began operating in a specific year or time period.
-
D.
originalHospitalClosed
Indicates that the hospital where an event or treatment originally took place has since ceased operations or been shut down.
-
E.
containsHospital
Indicates that one entity includes or encompasses a hospital within its boundaries or composition.
- 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_69e25d268a50819095f2fd479da8ef3f |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1a3b9287481908fd86c41f6d9fc53 |
completed | April 29, 2026, 6:22 a.m. |
| PD | Predicate disambiguation | batch_69f061dde2e481908308952f9c0d3c2e |
completed | April 28, 2026, 7:29 a.m. |
| PDg | Predicate description generation | batch_69f07cbbd7488190ab3c8ae7d0fb68bf |
completed | April 28, 2026, 9:24 a.m. |
Created at: April 17, 2026, 5:34 p.m.