Triple
T3868781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tennessee–Alabama border |
E91925
|
entity |
| Predicate | terrainTypeAlongBorder |
P9701
|
FINISHED |
| Object | rural areas |
—
|
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: rural areas | Statement: [Tennessee–Alabama border, terrainTypeAlongBorder, rural areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: terrainTypeAlongBorder Context triple: [Tennessee–Alabama border, terrainTypeAlongBorder, rural areas]
-
A.
terrainFeature
Indicates a relationship where one entity is a natural or constructed landform or surface characteristic associated with a given location or area.
-
B.
landscapeType
chosen
Indicates the kind or category of natural terrain or scenery that characterizes a place or area.
-
C.
hillType
Indicates the specific classification or category of a hill based on its form, characteristics, or context.
-
D.
territoryType
Indicates the specific kind or classification of a territory associated with an entity (e.g., country, region, zone, or jurisdiction type).
-
E.
sharesBorderType
Indicates that two entities are connected by a common boundary characterized by the same specified type of border (e.g., land, river, maritime).
- 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_69aed9645f348190a9868e7cef56ab7e |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec3dfb3c8190a0a07070f0d76bf6 |
completed | March 9, 2026, 3:50 p.m. |
| PD | Predicate disambiguation | batch_69aee754dddc8190936e1f9c40a770db |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:20 p.m.