Triple
T544526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julia Louis-Dreyfus |
E12701
|
entity |
| Predicate | hasMedicalCondition |
P1005
|
FINISHED |
| Object | breast cancer (diagnosed 2017) |
—
|
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: breast cancer (diagnosed 2017) | Statement: [Julia Louis-Dreyfus, hasMedicalCondition, breast cancer (diagnosed 2017)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMedicalCondition Context triple: [Julia Louis-Dreyfus, hasMedicalCondition, breast cancer (diagnosed 2017)]
-
A.
hasHealthConcern
Indicates that an entity has a specific health-related issue, condition, or concern associated with it.
-
B.
diagnosedWith
chosen
Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
-
C.
diseaseType
Indicates that one entity is classified as a specific type or category of disease in relation to another entity.
-
D.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
-
E.
mayBeComorbidWith
Indicates that two conditions or disorders can occur together in the same individual, potentially influencing each other’s presence or severity.
- 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_69a49334226c81908b0ea1689ef6aa3f |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a498dea88881908a938fe8f2313bec |
completed | March 1, 2026, 7:51 p.m. |
| PD | Predicate disambiguation | batch_69a494b8098481908097228db8ad0262 |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:32 p.m.