Triple
T8497210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BSF Academy Tekanpur |
E201128
|
entity |
| Predicate | securityForceTypeTrained |
P40765
|
FINISHED |
| Object | border guarding force |
—
|
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: border guarding force | Statement: [BSF Academy Tekanpur, securityForceTypeTrained, border guarding force]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: securityForceTypeTrained Context triple: [BSF Academy Tekanpur, securityForceTypeTrained, border guarding force]
-
A.
securityCooperatesWith
Indicates that one security-related entity collaborates or works jointly with another on security matters or activities.
-
B.
providesTrainingFor
chosen
Indicates that one entity delivers or conducts training activities intended to develop the skills or knowledge of another entity.
-
C.
hasTrainingComplex
Indicates that an entity possesses or is associated with a dedicated facility or complex used for training activities.
-
D.
hasTrainingType
Indicates that an entity is associated with or characterized by a specific type or category of training.
-
E.
securityApparatusRole
Indicates the role or function an entity holds within a security or law-enforcement apparatus.
- 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_69ca831ee390819095fae73400bbfafc |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe57f8c508190b3a93ef180db9873 |
completed | March 31, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69cbd10a4b0881909e254117780dc823 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:13 p.m.