Triple
T3271026
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ponta Delgada |
E68647
|
entity |
| Predicate | municipalPopulation |
P38055
|
FINISHED |
| Object | approximately 68,000 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: approximately 68,000 inhabitants | Statement: [Ponta Delgada, municipalPopulation, approximately 68,000 inhabitants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: municipalPopulation Context triple: [Ponta Delgada, municipalPopulation, approximately 68,000 inhabitants]
-
A.
municipalityPopulation
chosen
Indicates the total number of inhabitants living within a given municipality.
-
B.
permanentPopulation
Indicates that an entity has a stable, long-term resident population rather than a temporary or transient presence.
-
C.
cityPopulationContext
Indicates the contextual relationship between a city and information about its population, such as size, distribution, or demographic characteristics.
-
D.
populationDemonym
Indicates the term used to refer to the people or inhabitants associated with a particular place or region.
-
E.
metropolitanAreaPopulationApproximate
Indicates that the predicate specifies an approximate total population size for a given metropolitan area.
- 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_69ad859b54f881909bf530d549caf2fd |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adaff4b9dc8190b7e3da0bbffccf99 |
completed | March 8, 2026, 5:20 p.m. |
| PD | Predicate disambiguation | batch_69ada41d7eac8190ada4bf5f793d5c49 |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:09 p.m.