Triple
T13194434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hôpital d'instruction des armées Clermont-Tonnerre |
E314074
|
entity |
| Predicate | hasSurgicalDepartment |
P41890
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Hôpital d'instruction des armées Clermont-Tonnerre, hasSurgicalDepartment, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSurgicalDepartment Context triple: [Hôpital d'instruction des armées Clermont-Tonnerre, hasSurgicalDepartment, yes]
-
A.
surgicalField
Indicates the specific anatomical area or region of the body on which a surgical procedure is performed.
-
B.
operatedTheatersIn
Indicates that an entity managed or ran the day-to-day operations of one or more theaters in a specified location or context.
-
C.
hasClinicalUnit
chosen
Indicates that an entity is associated with or belongs to a specific clinical unit or department within a healthcare setting.
-
D.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
-
E.
hasPharmacyDepartment
Indicates that an entity includes or is associated with a dedicated pharmacy department or unit.
- 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_69d806ae1e08819090d95bfe1538cc17 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98cf054f88190b05ced98d5a22a62 |
completed | April 10, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69d98bc6bc108190b5a6a265bf6e9fd4 |
completed | April 10, 2026, 11:46 p.m. |
Created at: April 9, 2026, 9:16 p.m.