Triple
T12220834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loiceño |
E291209
|
entity |
| Predicate | associatedMunicipalityPopulationDemonym |
P49650
|
FINISHED |
| Object | Loíza inhabitants |
—
|
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: Loíza inhabitants | Statement: [Loiceño, associatedMunicipalityPopulationDemonym, Loíza inhabitants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedMunicipalityPopulationDemonym Context triple: [Loiceño, associatedMunicipalityPopulationDemonym, Loíza inhabitants]
-
A.
populationDemonym
Indicates the term used to refer to the people or inhabitants associated with a particular place or region.
-
B.
municipalityPopulation
Indicates the total number of inhabitants living within a given municipality.
-
C.
relatedDemonym
chosen
Indicates that one entity is the demonym (name for residents or natives) associated with the other entity.
-
D.
isMostPopulousMunicipalityOf
Indicates that a municipality has the largest population among all municipalities within the specified administrative area or region.
-
E.
permanentPopulation
Indicates that an entity has a stable, long-term resident population rather than a temporary or transient presence.
- 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_69d6ab668acc8190963ba424049d6aee |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d920e312708190b4aede2e21f5f697 |
completed | April 10, 2026, 4:10 p.m. |
| PD | Predicate disambiguation | batch_69d91c3d669c81908eea7ad61122d275 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:51 p.m.