Triple
T4862200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas Medical Center |
E108686
|
entity |
| Predicate | numberOfAnnualPatientVisits |
P59460
|
FINISHED |
| Object | 10000000+ |
—
|
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: 10000000+ | Statement: [Texas Medical Center, numberOfAnnualPatientVisits, 10000000+]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAnnualPatientVisits Context triple: [Texas Medical Center, numberOfAnnualPatientVisits, 10000000+]
-
A.
annualVisitation
Indicates a recurring visit or attendance that takes place once every year between the related entities.
-
B.
numberOfHospitalized
Indicates the count of individuals who have been admitted to a hospital for medical care.
-
C.
visitorFrequency
Indicates how often a visitor comes to or interacts with a particular entity or location.
-
D.
hasPatient
Indicates that an action, event, or process involves a specific entity as the one undergoing or receiving its effects (the patient).
-
E.
doctorNumber
Indicates the unique identifying number assigned to a doctor in the context of a relationship or record.
- 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_69bd440b965081908b0557721cae6338 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6d5f62b48190b367ed1b850cfbcb |
completed | March 20, 2026, 3:53 p.m. |
| PD | Predicate disambiguation | batch_69bd6c27334481909ba8ac80854f7d8e |
completed | March 20, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69bd6cfa8bd881908e376ab286759cc2 |
completed | March 20, 2026, 3:51 p.m. |
Created at: March 20, 2026, 1:26 p.m.