Triple
T8380877
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weihsien Internment Camp |
E197683
|
entity |
| Predicate | hasLanguageOfInternees |
P35567
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Weihsien Internment Camp, hasLanguageOfInternees, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageOfInternees Context triple: [Weihsien Internment Camp, hasLanguageOfInternees, English]
-
A.
hasOfficerLanguage
Indicates that an officer is able or authorized to communicate in a specified language.
-
B.
languageOfInterpretation
Indicates the language in which something (such as text, speech, or content) is interpreted or understood.
-
C.
eligibleLanguage
Indicates that a particular language satisfies the required conditions to be considered valid or allowed in a given context.
-
D.
hasLanguages
chosen
Indicates that an entity is associated with one or more languages it uses, supports, or is expressed in.
-
E.
hasClericalLanguage
Indicates that something is expressed using formal, religious, or church-related language or terminology.
- 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_69ca82f64c188190af4e1608036b865d |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb80c57080819097eef2b7e46eaaee |
completed | March 31, 2026, 8:07 a.m. |
| PD | Predicate disambiguation | batch_69cb70cfe82881909fe374ba52649e84 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:02 p.m.