Triple
T1030462
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Borja |
E22237
|
entity |
| Predicate | safetyLevel |
P3842
|
FINISHED |
| Object | relatively high compared to other Lima districts |
—
|
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: relatively high compared to other Lima districts | Statement: [San Borja, safetyLevel, relatively high compared to other Lima districts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyLevel Context triple: [San Borja, safetyLevel, relatively high compared to other Lima districts]
-
A.
safety
Indicates that an entity provides, ensures, or is associated with protection from harm, danger, or risk for another entity or within a given context.
-
B.
safetyRequirement
Indicates that one entity specifies or imposes conditions, standards, or measures necessary to ensure the safety of another entity or activity.
-
C.
riskLevel
chosen
Indicates the degree of potential harm, loss, or adverse outcome associated with a particular situation, action, or entity.
-
D.
amenityLevel
Indicates the degree or quality of facilities, services, or conveniences provided in relation to something.
-
E.
protectionLevel
Indicates the degree or extent to which something is safeguarded against harm, risk, or unauthorized access.
- 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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b95d35888190a20593a278175df7 |
completed | March 1, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69a4b7276180819085c6b23501a6a6e0 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:41 p.m.