Triple
T33226349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shelby White |
E850561
|
entity |
| Predicate | hasMadeDonationsTo |
P202912
|
FINISHED |
| Object | museums |
—
|
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: museums | Statement: [Shelby White, hasMadeDonationsTo, museums]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMadeDonationsTo Context triple: [Shelby White, hasMadeDonationsTo, museums]
-
A.
hasDonationsFrom
Indicates that an entity receives donations or contributions from another entity.
-
B.
hasDonationsDirected
Indicates that donations are purposefully allocated or directed from one entity to another or toward a specific target or cause.
-
C.
hasDonatedCollectionTo
Indicates that one entity has given a collection of items, typically as a donation, to another entity.
-
D.
donatedWith
Indicates that an entity made a donation using, accompanied by, or in association with a particular method, item, or context.
-
E.
donationResultedIn
Indicates that a donation directly caused or brought about a particular outcome or consequence.
- 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_69f3496083dc8190b229bb6932dc548b |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a00cda99c908190980e0bf54cb2e2a4 |
completed | May 10, 2026, 6:25 p.m. |
| PD | Predicate disambiguation | batch_6a00cd1635b08190a791ecfcf87a1d54 |
completed | May 10, 2026, 6:23 p.m. |
| PDg | Predicate description generation | batch_6a00cda8ed348190950ee34bb24ae65b |
completed | May 10, 2026, 6:25 p.m. |
Created at: May 1, 2026, 1:30 a.m.