Triple
T27316339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbara Zucker |
E689356
|
entity |
| Predicate | hasNotablePhilanthropy |
P134987
|
FINISHED |
| Object | medical education |
—
|
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: medical education | Statement: [Barbara Zucker, hasNotablePhilanthropy, medical education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotablePhilanthropy Context triple: [Barbara Zucker, hasNotablePhilanthropy, medical education]
-
A.
hasNotablePhilanthropicFocus
chosen
Indicates that an entity’s philanthropic activities are significantly directed toward a particular cause, issue, or beneficiary group.
-
B.
hasPhilanthropicRole
Indicates that an entity holds or performs a role related to charitable, philanthropic, or socially beneficial activities.
-
C.
isPhilanthropic
Indicates that an entity habitually engages in generous actions or donations intended to promote the welfare of others.
-
D.
hasNotableNamesakeContribution
Indicates that an entity has made a significant contribution specifically related to or inspired by its notable namesake.
-
E.
hasPhilanthropicFounder
Indicates that an entity has a founder who is actively engaged in philanthropy or charitable giving.
- 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_69ef355c53a08190a8a92e355a7ce115 |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69fe6c811bcc81908b1e1b1f8bcb071b |
completed | May 8, 2026, 11:06 p.m. |
| PD | Predicate disambiguation | batch_69fe6c026d5481908b7a814dcf38c183 |
completed | May 8, 2026, 11:04 p.m. |
Created at: April 27, 2026, 11:30 a.m.