Triple
T34390236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christina M. Hixson |
E882676
|
entity |
| Predicate | hasGivenAmount |
P50434
|
FINISHED |
| Object | tens of millions of dollars in charitable donations |
—
|
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: tens of millions of dollars in charitable donations | Statement: [Christina M. Hixson, hasGivenAmount, tens of millions of dollars in charitable donations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGivenAmount Context triple: [Christina M. Hixson, hasGivenAmount, tens of millions of dollars in charitable donations]
-
A.
hasGiven
chosen
Indicates that one entity has transferred or presented something to another entity.
-
B.
owedMoneyTo
Indicates that one entity has a financial obligation or debt that must be paid to another entity.
-
C.
hasGivenNumber
Indicates that an entity is associated with or assigned a specific number.
-
D.
hasMonetaryGrant
Indicates that an entity provides or receives a monetary grant from another entity.
-
E.
receivedSettlementAmount
Indicates that an entity has obtained a specified amount of money or value as part of a settlement.
- 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_69f349c0219881909393bbbc1edc8161 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71c35327c8190884f1bfe12bd2cd7 |
completed | May 3, 2026, 9:58 a.m. |
| PD | Predicate disambiguation | batch_69f71822d0e88190ac9731c7ae5a4def |
completed | May 3, 2026, 9:40 a.m. |
Created at: May 1, 2026, 1:59 a.m.