Triple
T19617035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Exeter, Pennsylvania |
E470888
|
entity |
| Predicate | isInCountySeatArea |
P40853
|
FINISHED |
| Object | Luzerne County, Pennsylvania |
—
|
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: Luzerne County, Pennsylvania | Statement: [Exeter, Pennsylvania, isInCountySeatArea, Luzerne County, Pennsylvania]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isInCountySeatArea Context triple: [Exeter, Pennsylvania, isInCountySeatArea, Luzerne County, Pennsylvania]
-
A.
isInCountySeatCity
Indicates that a given location or entity is situated within the city that serves as the county seat (administrative center) of a county.
-
B.
isInCountySeat
chosen
Indicates that one entity (typically a place or facility) is located within the county seat of a given county.
-
C.
isInCountySeatJurisdiction
Indicates that one entity falls under the legal or administrative authority of the county seat associated with another entity.
-
D.
isInCountySeatMetroArea
Indicates that an entity is located within the metropolitan area of a county seat.
-
E.
isInCountySeatAreaOfInfluence
Indicates that one location lies within the geographic or functional area of influence of a county seat.
- 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_69d8e510fa248190b7afb274a1d4cf73 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e640e272808190800c5bccccd64753 |
completed | April 20, 2026, 3:06 p.m. |
| PD | Predicate disambiguation | batch_69e514e5cb108190ae260e466c447314 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 1:43 p.m.