Triple
T23252933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deke Slayton |
E581782
|
entity |
| Predicate | effectOfMedicalCondition |
P129457
|
FINISHED |
| Object | grounded from spaceflight for many years |
—
|
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: grounded from spaceflight for many years | Statement: [Deke Slayton, effectOfMedicalCondition, grounded from spaceflight for many years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOfMedicalCondition Context triple: [Deke Slayton, effectOfMedicalCondition, grounded from spaceflight for many years]
-
A.
healthEffect
Indicates the impact or consequence that one entity has on the health or well-being of another.
-
B.
clinicalCondition
chosen
Indicates that one entity has, exhibits, or is associated with a particular medical or health-related condition described by the other entity.
-
C.
settingOfIllness
Indicates the context, environment, or circumstances in which an illness occurs or manifests.
-
D.
hasPharmacologicalEffect
Indicates that one entity produces a specific pharmacological effect or action on another entity.
-
E.
effectOnSystem
Indicates the influence, change, or impact that one entity, action, or condition has on the state or behavior of a system.
- 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_69e24606b17c81908aba1a4911c8a8ba |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f193f840dc819098e52272abefe616 |
completed | April 29, 2026, 5:15 a.m. |
| PD | Predicate disambiguation | batch_69effce4d704819092826931d430e8c4 |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:11 p.m.