Triple
T19483339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nawab Shah Jahan Begum |
E487445
|
entity |
| Predicate | focusOfPolicies |
P1876
|
FINISHED |
| Object | administrative efficiency |
—
|
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: administrative efficiency | Statement: [Nawab Shah Jahan Begum, focusOfPolicies, administrative efficiency]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: focusOfPolicies Context triple: [Nawab Shah Jahan Begum, focusOfPolicies, administrative efficiency]
-
A.
policyFocus
chosen
Indicates that an entity (such as a person, organization, or document) is primarily concerned with, directed toward, or centered on a particular policy area or issue.
-
B.
focusOf
Indicates that one entity is the primary subject, target, or center of attention, activity, or interest for another entity.
-
C.
focusType
Indicates the specific kind or category of focus or attention that is being applied to or associated with an entity or interaction.
-
D.
focusesOn
Indicates that one entity directs its attention, effort, or primary activity toward another entity or specific subject.
-
E.
focusIssue
Indicates that an issue, topic, or problem is the primary subject of attention or concern in a given context.
- 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_69d8e8d924388190b847cb15bb3d0aff |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6343c23308190a35f462f95338651 |
completed | April 20, 2026, 2:12 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7883308190b73912a71a35a835 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:39 p.m.