Triple
T18316213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Office of Redress Administration |
E438759
|
entity |
| Predicate | totalPayout |
P9015
|
FINISHED |
| Object | over 1.6 billion 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: over 1.6 billion US dollars | Statement: [Office of Redress Administration, totalPayout, over 1.6 billion US dollars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalPayout Context triple: [Office of Redress Administration, totalPayout, over 1.6 billion US dollars]
-
A.
revenuePaidTo
chosen
Indicates that a specified amount of revenue is paid or transferred from one entity to another as a recipient.
-
B.
totalPurse
Indicates the total amount of prize money or winnings available in a given competitive event or context.
-
C.
totalGross
Indicates the overall amount of money generated in revenue, typically from all sources over a specified period or for a specific work or event.
-
D.
payingPlayers
Indicates that one or more entities are making a payment to one or more players, typically as compensation, fees, or rewards.
-
E.
topPrizePayoutForm
Indicates the method or structure by which the highest prize in a contest, lottery, or similar event is paid out.
- 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_69d8b916a2d081909e249e4902f6aad9 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5021e61008190a300b6c51976a837 |
completed | April 19, 2026, 4:26 p.m. |
| PD | Predicate disambiguation | batch_69e44fe4ee10819086b4142444fca1f5 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:36 a.m.