Triple
T3624873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marla Lerner Tanenbaum |
E76812
|
entity |
| Predicate | genreOfCharitableWork |
P3801
|
FINISHED |
| Object | education-related philanthropy |
—
|
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: education-related philanthropy | Statement: [Marla Lerner Tanenbaum, genreOfCharitableWork, education-related philanthropy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genreOfCharitableWork Context triple: [Marla Lerner Tanenbaum, genreOfCharitableWork, education-related philanthropy]
-
A.
genreOfPhilanthropy
chosen
Indicates the specific type or category of philanthropic activity to which an act, initiative, or organization belongs.
-
B.
nonprofitType
Indicates the specific category or classification of a nonprofit organization based on its legal or functional type.
-
C.
fieldOfPhilanthropy
Indicates that an entity is engaged in or associated with a particular area or domain of philanthropic activity.
-
D.
associatedCharity
Indicates that one entity has a formal or recognized charitable affiliation or partnership with another entity.
-
E.
grantmakingType
Indicates the specific category or method by which grants are awarded or administered in a grantmaking relationship.
- 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_69ad85dc03948190b35b7189e4175bcc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc2d9845c8190ad65b2471000dfa0 |
completed | March 8, 2026, 6:41 p.m. |
| PD | Predicate disambiguation | batch_69adb8410a5881909c94818d7060b2b0 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:23 p.m.