Triple
T19692643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lippe (region) |
E472873
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Horn-Bad Meinberg |
—
|
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: Horn-Bad Meinberg | Statement: [Lippe (region), hasPart, Horn-Bad Meinberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Horn-Bad Meinberg Context triple: [Lippe (region), hasPart, Horn-Bad Meinberg]
-
A.
Horn-Bad Meinberg
chosen
Horn-Bad Meinberg is a spa and resort town in North Rhine-Westphalia, Germany, known for its health tourism and natural surroundings.
-
B.
Hornsberg
Hornsberg is a waterfront residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
-
C.
Hornberg, Germany
Hornberg, Germany is a small town in the Black Forest region of Baden-Württemberg known for its traditional industry and scenic surroundings.
-
D.
Bad Schussenried
Bad Schussenried is a spa town in southern Germany known for its historic monastery complex and scenic location in Upper Swabia.
-
E.
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.
- 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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e64210cddc8190836faa2996a44457 |
completed | April 20, 2026, 3:11 p.m. |
Created at: April 10, 2026, 1:46 p.m.