Triple
T28515525
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Furman family (NYU Law donors) |
E721603
|
entity |
| Predicate | beneficiaryInstitutionType |
P172377
|
FINISHED |
| Object | law school |
—
|
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: law school | Statement: [Furman family (NYU Law donors), beneficiaryInstitutionType, law school]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: beneficiaryInstitutionType Context triple: [Furman family (NYU Law donors), beneficiaryInstitutionType, law school]
-
A.
beneficiaryInstitution
Indicates that an institution is the recipient or beneficiary of an action, service, or resource.
-
B.
beneficiaryType
Indicates the type or category of beneficiary that receives or is intended to receive the benefit or outcome of an action or resource.
-
C.
beneficiaryCountry
Indicates that one country is the recipient or beneficiary of aid, resources, or advantages provided in a given context.
-
D.
insuredInstitutionType
Indicates the type or category of institution that is covered under an insurance arrangement or policy.
-
E.
beneficiarySelectionMethod
Indicates the way in which a beneficiary is chosen or determined in relation to an action, agreement, or resource.
- 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_69f01a5c072081908c7b04bcf6478da9 |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f6abe15d5c81909ccf4ce37f78bc43 |
completed | May 3, 2026, 1:58 a.m. |
| PD | Predicate disambiguation | batch_69f6aa1c555081908787dbf76147f180 |
completed | May 3, 2026, 1:51 a.m. |
| PDg | Predicate description generation | batch_69f6aaf31a548190b2f792ff4b8c002a |
completed | May 3, 2026, 1:54 a.m. |
Created at: April 28, 2026, 3:16 a.m.