Triple
T20672617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fredericksburg, Lebanon County, Pennsylvania |
E508066
|
entity |
| Predicate | hasWaterAreaSquareMilesApprox |
P475
|
FINISHED |
| Object | 0.0 |
—
|
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: 0.0 | Statement: [Fredericksburg, Lebanon County, Pennsylvania, hasWaterAreaSquareMilesApprox, 0.0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWaterAreaSquareMilesApprox Context triple: [Fredericksburg, Lebanon County, Pennsylvania, hasWaterAreaSquareMilesApprox, 0.0]
-
A.
hasAreaWaterBody
Indicates that an entity includes, contains, or is associated with a body of water within its area or boundaries.
-
B.
drainageAreaApprox
Indicates that one entity has an approximate drainage area measured or characterized by the other entity.
-
C.
areaWater
chosen
Indicates the relationship between a geographic entity and the total area of its surface that is covered by water.
-
D.
areaApproximateSquareMiles
Indicates that the related entity has an area approximately equal to the specified number of square miles.
-
E.
landAreaSquareMiles
Indicates the size of a geographic area measured in square miles.
- 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_69e0b4c1164881909a3bf1e3ddb2bc32 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b5cb1fc88190805f623e93a70368 |
completed | April 20, 2026, 11:24 p.m. |
| PD | Predicate disambiguation | batch_69e5c03caee881908be4dd25796a03d5 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 11:44 a.m.