Triple
T21379263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stadshagen |
E527298
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Hornsberg |
—
|
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: Hornsberg | Statement: [Stadshagen, adjacentTo, Hornsberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hornsberg Context triple: [Stadshagen, adjacentTo, Hornsberg]
-
A.
Hornsberg
chosen
Hornsberg is a waterfront residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
-
B.
Hornberg
Hornberg is a small town in the Black Forest region of Baden-Württemberg, Germany, known for its scenic landscape and traditional cuckoo clock craftsmanship.
-
C.
Geiersthal
Geiersthal is a small municipality in the Bavarian Forest region of southeastern Germany.
-
D.
Hangelsberg
Hangelsberg is a village in the German state of Brandenburg, known as a district of the municipality Grünheide (Mark) in the Oder-Spree region.
-
E.
Langenhorn
Langenhorn is a residential quarter in the northern part of Hamburg, Germany, known for its green spaces and suburban 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_69e0b51f363c8190944000ab5523b02b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0cc2b5c8190aa5f20f920523fe9 |
completed | April 22, 2026, 11:28 a.m. |
Created at: April 16, 2026, 5:11 p.m.