Triple
T32488878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greater Asunción |
E830322
|
entity |
| Predicate | hasMajorSuburbanCenter |
P17255
|
FINISHED |
| Object | San Lorenzo |
—
|
NE NERFINISHED |
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: San Lorenzo | Statement: [Greater Asunción, hasMajorSuburbanCenter, San Lorenzo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMajorSuburbanCenter Context triple: [Greater Asunción, hasMajorSuburbanCenter, San Lorenzo]
-
A.
hasMajorSuburb
chosen
Indicates that a larger urban area includes or is associated with a specific major suburb within its boundaries or sphere.
-
B.
hasSuburbanAreas
Indicates that a place includes or is associated with surrounding residential suburban districts or neighborhoods.
-
C.
isMajorResidentialCenterOf
Indicates that a location serves as a primary hub or main area of residence for the referenced population or region.
-
D.
hasSuburbanMunicipality
Indicates that one administrative region includes or is associated with a municipality located in a suburban area.
-
E.
hasMajorCity
Indicates that a location possesses at least one city of significant size, importance, or influence within its region or country.
- 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_69f34920aa4081908d8fb0277414b911 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a009273634c8190b5b9c87053b56760 |
completed | May 10, 2026, 2:13 p.m. |
| PD | Predicate disambiguation | batch_6a0092171230819096e4274dd97e0410 |
completed | May 10, 2026, 2:11 p.m. |
Created at: May 1, 2026, 12:58 a.m.