Triple
T11980788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Marino Foundation |
E285151
|
entity |
| Predicate | focusesOnAgeGroup |
P29430
|
FINISHED |
| Object | children with autism |
—
|
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: children with autism | Statement: [Dan Marino Foundation, focusesOnAgeGroup, children with autism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: focusesOnAgeGroup Context triple: [Dan Marino Foundation, focusesOnAgeGroup, children with autism]
-
A.
ageGroup
Indicates the categorical age range or bracket to which an entity belongs.
-
B.
attractsAgeGroup
Indicates that something tends to draw interest or appeal from people belonging to a particular age group.
-
C.
hasAgeFocus
chosen
Indicates a relationship where something is characterized or distinguished by a particular age group or age-related emphasis.
-
D.
ageGroupRole
Indicates the role or function an entity has within a specific age group classification.
-
E.
ageGroupStructure
Indicates how a population or set of entities is distributed across different age groups or age categories.
- 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_69d6ab2eaeb881909f7914758f859413 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903acbb9081908fe7f8360057785c |
completed | April 10, 2026, 2:05 p.m. |
| PD | Predicate disambiguation | batch_69d902abca70819098291aa51b593708 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:46 p.m.