Triple
T10155599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David H. Koch Theater |
E232767
|
entity |
| Predicate | donationAmount |
P499
|
FINISHED |
| Object | 100 million US dollars |
—
|
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: 100 million US dollars | Statement: [David H. Koch Theater, donationAmount, 100 million US dollars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: donationAmount Context triple: [David H. Koch Theater, donationAmount, 100 million US dollars]
-
A.
philanthropicDonation
Indicates that one entity voluntarily gives money, goods, or services to another entity for charitable or public-benefit purposes without expecting direct compensation.
-
B.
owedMoneyTo
Indicates that one entity has a financial obligation or debt that must be paid to another entity.
-
C.
donated
chosen
Indicates that one entity voluntarily gave something of value (such as money, goods, or time) to another entity, typically without expecting anything in return.
-
D.
typeOfDonations
Indicates the kinds or categories of donations involved in a given context or transaction.
-
E.
raisedMoneyFor
Indicates that one entity collected or obtained funds specifically to support or benefit another entity, cause, or purpose.
- 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_69ca84885e48819088a31b127cf44904 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cdec3a5e7c819098b2f9ccbde7cf94 |
completed | April 2, 2026, 4:10 a.m. |
| PD | Predicate disambiguation | batch_69cd4ba795808190acc9124c98c6e40f |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9:09 p.m.