Triple
T482081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The David and Lucile Packard Foundation |
E9189
|
entity |
| Predicate | funderType |
P14892
|
FINISHED |
| Object | grantmaking foundation |
—
|
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: grantmaking foundation | Statement: [The David and Lucile Packard Foundation, funderType, grantmaking foundation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: funderType Context triple: [The David and Lucile Packard Foundation, funderType, grantmaking foundation]
-
A.
fundedBy
Indicates that an entity receives financial support or resources from another entity.
-
B.
grantmakingType
Indicates the specific category or method by which grants are awarded or administered in a grantmaking relationship.
-
C.
fundingModel
Indicates how an entity is financially supported or sustained, such as through specific revenue sources, payment structures, or funding mechanisms.
-
D.
foundedOrganizationType
Indicates that an entity established or created an organization of a specified type (e.g., company, nonprofit, institution).
-
E.
sponsoringOrganizationType
Indicates the kind or category of organization that provides sponsorship or support in the described relationship or activity.
- 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_69a2e7ff81708190b0507a24a997232c |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f05a7f6c819082b4a5a3e69468a6 |
completed | Feb. 28, 2026, 1:40 p.m. |
| PD | Predicate disambiguation | batch_69a2edf321288190b5d560f75782c2cb |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2ef4030608190b39852b347a505ca |
completed | Feb. 28, 2026, 1:36 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.