Triple
T1030453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Borja |
E22237
|
entity |
| Predicate | hasGreenSpacePolicy |
P7262
|
FINISHED |
| Object | emphasis on parks and green areas |
—
|
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: emphasis on parks and green areas | Statement: [San Borja, hasGreenSpacePolicy, emphasis on parks and green areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGreenSpacePolicy Context triple: [San Borja, hasGreenSpacePolicy, emphasis on parks and green areas]
-
A.
hasPolicyArea
chosen
Indicates that an entity (such as a policy, program, or initiative) is associated with or pertains to a specific policy area or domain.
-
B.
hasParks
Indicates that one entity possesses, contains, or is associated with one or more parks.
-
C.
hasRegionalPark
Indicates that a place or administrative area contains or is served by a designated regional park.
-
D.
hasRegionalPlanningOrganization
Indicates that an entity is associated with or served by a specific regional planning organization responsible for coordinated planning activities in that area.
-
E.
commonPolicyArea
Indicates that two entities share the same policy domain, topic, or area of regulatory or legislative focus.
- 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.