Triple
T36866592
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roy L. "Rocky" Dennis |
E911099
|
entity |
| Predicate | prognosisAtChildhood |
P24568
|
FINISHED |
| Object | doctors predicted he would not live past early childhood |
—
|
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: doctors predicted he would not live past early childhood | Statement: [Roy L. "Rocky" Dennis, prognosisAtChildhood, doctors predicted he would not live past early childhood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: prognosisAtChildhood Context triple: [Roy L. "Rocky" Dennis, prognosisAtChildhood, doctors predicted he would not live past early childhood]
-
A.
hasPrognosis
chosen
Indicates that one entity (typically a medical condition or case) is associated with an expected course or outcome over time, such as likely progression, duration, or chances of recovery.
-
B.
diedInChildhood
Indicates that the person died before reaching adulthood, during their childhood years.
-
C.
timePeriodOfChildhood
Indicates the span of time during which an entity is considered to have been in its childhood phase.
-
D.
childMedicalCondition
Indicates that a child has or is affected by a specified medical condition.
-
E.
diseaseCourse
Indicates the progression and temporal pattern of a disease in an individual or population, including onset, development, and outcome over time.
- 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_69f76e80f6f0819091cba8e19b269615 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f9fd6834cc8190aa27153d6a99f3bb |
completed | May 5, 2026, 2:23 p.m. |
| PD | Predicate disambiguation | batch_69f7cf7890008190a8bc355ff2d61c86 |
completed | May 3, 2026, 10:43 p.m. |
Created at: May 3, 2026, 4:13 p.m.