Triple
T9507746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joachim Ziemßen |
E229312
|
entity |
| Predicate | settingOfIllness |
P88456
|
FINISHED |
| Object | sanatorium in Davos |
—
|
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: sanatorium in Davos | Statement: [Joachim Ziemßen, settingOfIllness, sanatorium in Davos]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingOfIllness Context triple: [Joachim Ziemßen, settingOfIllness, sanatorium in Davos]
-
A.
causeOfIllness
Indicates that one entity is the reason or source responsible for another entity’s illness or disease.
-
B.
diseaseType
Indicates that one entity is classified as a specific type or category of disease in relation to another entity.
-
C.
conditions
Indicates that one entity specifies or imposes requirements, constraints, or circumstances that must be satisfied or hold true for another entity or situation.
-
D.
clinicalSignOf
Indicates that one clinical sign is evidence or manifestation of a particular disease, condition, or underlying medical state.
-
E.
featuresDisease
Indicates that an entity exhibits, presents, or is characterized by a particular disease.
- 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_69ca847611c48190a28c028644198c75 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd98543b1881908b537abdc1d2f9c0 |
completed | April 1, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69cca567ca448190bf4bcce8ce7dd54f |
completed | April 1, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69cca89d0f0c8190b4528990fe708fca |
completed | April 1, 2026, 5:09 a.m. |
Created at: March 30, 2026, 7:57 p.m.