Triple
T5565509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Summit County |
E145869
|
entity |
| Predicate | hasAreaClassification |
P6822
|
FINISHED |
| Object | metropolitan county |
—
|
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: metropolitan county | Statement: [Summit County, hasAreaClassification, metropolitan county]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaClassification Context triple: [Summit County, hasAreaClassification, metropolitan county]
-
A.
hasAreaType
chosen
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
B.
hasAreaRange
Indicates that something’s area falls within a specified minimum-to-maximum range.
-
C.
statisticalAreaClassification
Indicates how an area is categorized based on statistical criteria, such as population, density, or other quantitative measures, for analysis or reporting purposes.
-
D.
hasUrbanClassification
Indicates that an entity is assigned a specific urban status or category within a defined classification system.
-
E.
typeOfAreaRepresented
Indicates that one entity specifies the kind or category of area that another entity represents.
- 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_69c008fdae24819081aa002ad99cd966 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c02033cc308190895f13454c57f452 |
completed | March 22, 2026, 5 p.m. |
| PD | Predicate disambiguation | batch_69c01b12826c8190969a584d0f53aa44 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:36 p.m.