Triple
T28938846
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huntington's disease |
E730389
|
entity |
| Predicate | hasCure |
P184852
|
FINISHED |
| Object | no known cure |
—
|
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: no known cure | Statement: [Huntington's disease, hasCure, no known cure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCure Context triple: [Huntington's disease, hasCure, no known cure]
-
A.
hasRemedy
Indicates that one entity serves as a remedy, treatment, or corrective measure for a problem, condition, or undesirable state associated with another entity.
-
B.
curedWith
Indicates that one entity is treated or healed by using another entity as the remedy or therapeutic method.
-
C.
doesNotCure
Indicates that an action, treatment, or intervention fails to eliminate or resolve a condition, problem, or disease in the affected entity.
-
D.
hasDrug
Indicates that an entity possesses, is treated with, or is associated with a particular drug.
-
E.
hasReceivedTreatmentFor
Indicates that an entity has undergone or been given a treatment in relation to a specified condition, issue, or problem.
- 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_69f043ea0aa88190a25acbf46157995a |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69f7b628b17c8190aa058c1a51852a27 |
completed | May 3, 2026, 8:55 p.m. |
| PD | Predicate disambiguation | batch_69f7b4c06f5881908f0b98cad6796478 |
completed | May 3, 2026, 8:49 p.m. |
| PDg | Predicate description generation | batch_69f7b5cadd308190a864245a21b08f9a |
completed | May 3, 2026, 8:53 p.m. |
Created at: April 28, 2026, 8:34 a.m.