Triple
T4488336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darmstadt |
E107304
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | southern Hesse |
E115424
|
NE 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: southern Hesse | Statement: [Darmstadt, locatedIn, southern Hesse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: southern Hesse Context triple: [Darmstadt, locatedIn, southern Hesse]
-
A.
South Hesse
chosen
South Hesse is a region in the southern part of the German state of Hesse that includes major urban and economic centers such as Darmstadt and the Rhine-Main area.
-
B.
Northern Hesse region
The Northern Hesse region is a historical area in central Germany that once formed part of the territorial domain of the Prince of Waldeck.
-
C.
Middle Hesse
Middle Hesse is a central region of the German state of Hesse known for its mix of historic university towns, industrial centers, and rural landscapes.
-
D.
Giessenlanden
Giessenlanden was a former municipality in the Dutch province of South Holland that later became part of the newly formed municipality of Molenlanden.
-
E.
southwestern Germany
Southwestern Germany is a region of Germany known for its forested landscapes, wine-growing areas, and proximity to France and Switzerland.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69bd43f84f788190a1383579c4a595be |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd52ad36748190b791de458f2116b2 |
completed | March 20, 2026, 1:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd67a31d0c819089954c8af4b1bdd6 |
completed | March 20, 2026, 3:28 p.m. |
Created at: March 20, 2026, 12:59 p.m.