Triple
T9876715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MotoGP Czech Republic Grand Prix |
E240088
|
entity |
| Predicate | medicalSupport |
P73303
|
FINISHED |
| Object | circuit medical center |
—
|
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: circuit medical center | Statement: [MotoGP Czech Republic Grand Prix, medicalSupport, circuit medical center]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: medicalSupport Context triple: [MotoGP Czech Republic Grand Prix, medicalSupport, circuit medical center]
-
A.
medicalCar
Indicates that an entity is a vehicle used for providing medical transport or emergency medical services to another entity.
-
B.
emergencyOffice
Indicates that an office or location serves as an emergency contact point or coordination center for urgent or crisis situations.
-
C.
emergencyService
chosen
Indicates that one entity provides or is associated with urgent, time-critical assistance or response to another entity in emergency situations.
-
D.
medicalEvent
Indicates that a specific health-related occurrence or clinical incident has taken place involving one or more entities.
-
E.
focusesOnMedicalCare
Indicates that one entity directs attention, resources, or activity specifically toward providing or improving medical care for another entity.
- 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_69ca84e8a0788190b9061811d50fd554 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3fb58d481908407898912c4b4e9 |
completed | April 2, 2026, 12:10 a.m. |
| PD | Predicate disambiguation | batch_69cd1d810ed48190a252b70e9390c8f3 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:37 p.m.