Triple
T7689398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | War Relocation Authority |
E174206
|
entity |
| Predicate | numberOfCampsAdministered |
P56116
|
FINISHED |
| Object | 10 major War Relocation Centers |
—
|
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: 10 major War Relocation Centers | Statement: [War Relocation Authority, numberOfCampsAdministered, 10 major War Relocation Centers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCampsAdministered Context triple: [War Relocation Authority, numberOfCampsAdministered, 10 major War Relocation Centers]
-
A.
numberOfCamps
chosen
Indicates the total count of camps associated with or involved in a given entity or situation.
-
B.
hasNumberOfCampsites
Indicates the specific quantity of campsites associated with a given place, facility, or area.
-
C.
numberOfCampuses
Indicates the total count of campuses associated with a given entity.
-
D.
administersFrom
Indicates that an action, service, or process is carried out or managed starting from a particular source, location, or authority.
-
E.
numberOfCampaignsApprox
Indicates an approximate count of campaigns associated with or relevant to a given entity.
- 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_69c6995966348190939e6c37ba272c06 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c706d1f0208190bc5b695aa5736244 |
completed | March 27, 2026, 10:38 p.m. |
| PD | Predicate disambiguation | batch_69c70163dea88190ae729df50e63dfd7 |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:02 p.m.