Triple
T8591407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nigerien police |
E203434
|
entity |
| Predicate | trainingLanguage |
P36232
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [Nigerien police, trainingLanguage, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingLanguage Context triple: [Nigerien police, trainingLanguage, French]
-
A.
trainingDomain
Indicates that an entity is associated with or operates within a particular field, area, or domain of training.
-
B.
testLanguage
Indicates that an entity uses or is associated with a particular language for testing or evaluation purposes.
-
C.
languageOfTeachings
chosen
Indicates the language in which teachings, lessons, or instructional content are delivered or expressed.
-
D.
targetLanguage
Indicates the language that is the intended recipient or focus of a communication, translation, or linguistic operation.
-
E.
trainingComponent
Indicates that one entity functions as a training-related part, module, or element within a larger training process or system involving another 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_69ca832a7f108190b4e4f5648abf4aa2 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46c5e8888190b721e791c449b0df |
completed | March 31, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69cc454504448190aaad2af8b17357cd |
completed | March 31, 2026, 10:05 p.m. |
Created at: March 30, 2026, 6:23 p.m.