Triple
T22203206
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pennsylvania Dutch Country |
E548733
|
entity |
| Predicate | hasCounty |
P285
|
FINISHED |
| Object | Lebanon County |
—
|
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: Lebanon County | Statement: [Pennsylvania Dutch Country, hasCounty, Lebanon County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lebanon County Context triple: [Pennsylvania Dutch Country, hasCounty, Lebanon County]
-
A.
Lebanon County
chosen
Lebanon County is a county in south-central Pennsylvania known for its mix of rural farmland, small towns, and historical communities such as the city of Lebanon.
-
B.
Greene County
Greene County is a rural county in western Illinois known for its agricultural landscape and small communities.
-
C.
Greene County
Greene County is a rural county in central Iowa known for its agricultural landscape and small communities.
-
D.
Greene County
Greene County is a rural county in eastern New York State known for encompassing a significant portion of the scenic Catskill Mountains.
-
E.
Greene County
Greene County is a rural county in western Alabama known for its historical significance in the Black Belt region and its predominantly African American population.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e3ecc7c8190b5f94cd8f42e9d37 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12b25eac4819094b3c50027ed66db |
completed | April 28, 2026, 9:48 p.m. |
Created at: April 16, 2026, 8:36 p.m.