Triple
T205277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stop TB Partnership |
E4597
|
entity |
| Predicate | focusDisease |
P31
|
FINISHED |
| Object | tuberculosis |
—
|
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: tuberculosis | Statement: [Stop TB Partnership, focusDisease, tuberculosis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: focusDisease Context triple: [Stop TB Partnership, focusDisease, tuberculosis]
-
A.
diseaseType
Indicates that one entity is classified as a specific type or category of disease in relation to another entity.
-
B.
focusesOn
chosen
Indicates that one entity directs its attention, effort, or primary activity toward another entity or specific subject.
-
C.
diagnosedWith
Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
-
D.
focusPeriod
Indicates the specific time span during which attention, activity, or analysis is concentrated on something.
-
E.
symptom
Indicates that a particular condition, disease, or problem manifests through a specific observable sign or complaint.
- 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_69a25737567c81908f9c505300239181 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25f46b4f081909e5ee3718109a71f |
completed | Feb. 28, 2026, 3:21 a.m. |
| PD | Predicate disambiguation | batch_69a25b4b42ec8190bef16bbbdd30a742 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:51 a.m.