Triple
T4638548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unna |
E101592
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Werl |
E100762
|
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: Werl | Statement: [Unna, locatedNear, Werl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Werl Context triple: [Unna, locatedNear, Werl]
-
A.
Werl
chosen
Werl is a town in North Rhine-Westphalia, Germany, known for its historical significance and regional correctional facility.
-
B.
Bentheim
Bentheim is a historical county in Lower Saxony, Germany, known for its Reformed Protestant heritage and the former County of Bentheim.
-
C.
Lüdenscheid
Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
-
D.
Rudolfswerth
Rudolfswerth is the former German name for Novo Mesto, a historic town in southeastern Slovenia known for its medieval heritage and role as a regional cultural center.
-
E.
Meppen
Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
- 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_69bd43d3bc7c81908f81fcf380476b0f |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a64214481908a207e8070cc7a45 |
completed | March 20, 2026, 2:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be036d7aa081908b4b361dbae8ebc7 |
completed | March 21, 2026, 2:33 a.m. |
Created at: March 20, 2026, 1:13 p.m.