Triple
T4411388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japanese American internment |
E94859
|
entity |
| Predicate | compensationAmountPerSurvivor |
P25364
|
FINISHED |
| Object | 20000 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: 20000 US dollars | Statement: [Japanese American internment, compensationAmountPerSurvivor, 20000 US dollars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: compensationAmountPerSurvivor Context triple: [Japanese American internment, compensationAmountPerSurvivor, 20000 US dollars]
-
A.
pensionAmount
Indicates the specific monetary value of a pension that is assigned to or received by an entity.
-
B.
awardAmount
chosen
Indicates the specific quantity or value of an award that is granted in the context of a particular awarding event or relationship.
-
C.
maximumLifetimeBenefit
Indicates the greatest total amount of benefit that can be received over the entire duration of eligibility or coverage.
-
D.
typicalAwardAmount
Indicates the usual or most common amount of an award given in this relationship.
-
E.
memberRemuneration
Indicates that a member receives payment or compensation, typically for their role, services, or participation within an organization or group.
- 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_69b34539638c8190abfea3eb29425210 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b354e656dc819093ca8395d7334006 |
completed | March 13, 2026, 12:05 a.m. |
| PD | Predicate disambiguation | batch_69b34f5b36a881909bf2e970aa523390 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:29 p.m.