Triple
T4411377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japanese American internment |
E94859
|
entity |
| Predicate | affectedPeople |
P56117
|
FINISHED |
| Object | U.S. citizens of Japanese ancestry |
—
|
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: U.S. citizens of Japanese ancestry | Statement: [Japanese American internment, affectedPeople, U.S. citizens of Japanese ancestry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affectedPeople Context triple: [Japanese American internment, affectedPeople, U.S. citizens of Japanese ancestry]
-
A.
affectedPerson
Indicates that a particular person is impacted or influenced by an event, action, or condition.
-
B.
estimatedAffectedPeople
Indicates the estimated number of people expected to be impacted by a particular event, condition, or action.
-
C.
affectedCompany
Indicates that a company is impacted or influenced by a particular event, action, or entity.
-
D.
hasPeopleInvolved
Indicates that certain people participate in, are associated with, or are otherwise involved in the referenced entity or event.
-
E.
affectedCountry
Indicates that a particular country is impacted or influenced by an event, action, or condition.
- 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_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. |
| PDg | Predicate description generation | batch_69b3509997208190933f167f7e20ccfa |
completed | March 12, 2026, 11:47 p.m. |
Created at: March 12, 2026, 11:29 p.m.