Triple
T30480954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Minidoka War Relocation Center |
E775583
|
entity |
| Predicate | hasInmateOrigin |
P198491
|
FINISHED |
| Object | primarily from Washington |
—
|
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: primarily from Washington | Statement: [Minidoka War Relocation Center, hasInmateOrigin, primarily from Washington]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInmateOrigin Context triple: [Minidoka War Relocation Center, hasInmateOrigin, primarily from Washington]
-
A.
hasPerpetratorOrigin
Indicates that the origin or source of a perpetrator (such as their nationality, place of birth, or background) is associated with a particular event, action, or crime.
-
B.
hasFormerInmate
Indicates that an entity previously housed or supervised an individual who was once an inmate there.
-
C.
hasPrison
Indicates that one entity possesses, contains, or is the location of a prison associated with another entity.
-
D.
hasFormerCellmate
Indicates that one person was previously incarcerated in the same cell as another person.
-
E.
hasInmateLabor
Indicates that an entity utilizes or is associated with labor performed by incarcerated individuals.
- 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_69f22497341481909c21ba329fadaa6b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69feecf1bb248190ba30f0bb1d22ee08 |
completed | May 9, 2026, 8:14 a.m. |
| PD | Predicate disambiguation | batch_69feea5f27748190b223ee4e3ba5a678 |
completed | May 9, 2026, 8:03 a.m. |
| PDg | Predicate description generation | batch_69feecf102e08190b237c29e45beeca0 |
completed | May 9, 2026, 8:14 a.m. |
Created at: April 29, 2026, 8:12 p.m.