Triple
T21547229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duquesne |
E531656
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Dravosburg |
—
|
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: Dravosburg | Statement: [Duquesne, adjacentTo, Dravosburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dravosburg Context triple: [Duquesne, adjacentTo, Dravosburg]
-
A.
Dravosburg
chosen
Dravosburg is a small borough in Allegheny County, Pennsylvania, situated along the Monongahela River in the Pittsburgh metropolitan area.
-
B.
Treseburg
Treseburg is a small village in the Harz Mountains of central Germany, known as a scenic gateway for hiking and nature tourism in the surrounding Bode Valley.
-
C.
Callisburg
Callisburg is a small rural city located in Cooke County in northern Texas, United States.
-
D.
Isselburg
Isselburg is a small town in western North Rhine-Westphalia, Germany, near the Dutch border, known for its rural character and historic buildings.
-
E.
Loßburg
Loßburg is a municipality in the Black Forest region of southwestern Germany, known for its scenic landscapes and traditional Swabian character.
- 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_69e0c45f17148190949c330ab9c27706 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eeb58fb6608190a58cd00ecf560834 |
completed | April 27, 2026, 1:02 a.m. |
Created at: April 16, 2026, 6:28 p.m.