Triple
T18383623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seligenstadt |
E446520
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Hanau |
—
|
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: Hanau | Statement: [Seligenstadt, locatedNear, Hanau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanau Context triple: [Seligenstadt, locatedNear, Hanau]
-
A.
Hanau
chosen
Hanau is a town in the German state of Hesse, known as an important regional center and the birthplace of the Brothers Grimm.
-
B.
Harbach
Harbach is a surname most notably associated with Otto Harbach, an American lyricist and librettist of early 20th-century musical theatre.
-
C.
Hückeswagen
Hückeswagen is a small historic town in western Germany’s North Rhine-Westphalia, known for its medieval castle and location in the hilly Bergisches Land region.
-
D.
Benneckenstein
Benneckenstein is a small town in central Germany located in the Harz mountain region, known for its scenic landscapes and outdoor recreation.
-
E.
Henschhausen
Henschhausen is a small district or locality that forms part of the town of Bacharach in Rhineland-Palatinate, Germany.
- 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_69d8b9f370b88190b1e5081c2c238e7f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e5179da1048190944398e229e7a4c1 |
completed | April 19, 2026, 5:57 p.m. |
Created at: April 10, 2026, 10:45 a.m.