Triple
T5587809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Watertown, New York, United States |
E146797
|
entity |
| Predicate | countyPopulationCenter |
P2106
|
FINISHED |
| Object | Jefferson County, New York |
—
|
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: Jefferson County, New York | Statement: [Watertown, New York, United States, countyPopulationCenter, Jefferson County, New York]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countyPopulationCenter Context triple: [Watertown, New York, United States, countyPopulationCenter, Jefferson County, New York]
-
A.
majorPopulationCenter
Indicates that a location functions as a primary hub of population concentration and activity within a region.
-
B.
hasPopulationCenter
chosen
Indicates that an area, region, or administrative unit contains or is served by a primary settlement or population hub.
-
C.
hasPopulationCenterType
Indicates the classification of a population center by its type, such as city, town, village, or other settlement category.
-
D.
populationFocus
Indicates that something is primarily directed toward, concerned with, or designed for a particular population or demographic group.
-
E.
hasPopulationCenterDensity
Indicates the density of population centers within a given area or region.
- 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_69c009036c408190981a8d690b679b67 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0209e892c8190b936a05ef2a14d36 |
completed | March 22, 2026, 5:02 p.m. |
| PD | Predicate disambiguation | batch_69c01b16b9bc8190ab0b945507d90e05 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:38 p.m.