Triple
T13312461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lenne |
E317102
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Schmallenberg |
E537268
|
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: Schmallenberg | Statement: [Lenne, flowsThrough, Schmallenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schmallenberg Context triple: [Lenne, flowsThrough, Schmallenberg]
-
A.
Schmallenberg
chosen
Schmallenberg is a small town in the Hochsauerland district of North Rhine-Westphalia, Germany, known for its picturesque landscapes and tourism in the Sauerland region.
-
B.
Vircava
Vircava is a river in Latvia that serves as one of the tributaries feeding into the larger Lielupe River system.
-
C.
Marburg virus
Marburg virus is a highly lethal filovirus that causes severe hemorrhagic fever in humans and nonhuman primates, similar to Ebola.
-
D.
Bordet
Bordet is a railway station in Brussels, Belgium, serving local and regional train connections in the Schaerbeek area.
-
E.
Cervi
Cervi is an Italian surname most notably associated with Al Cervi, a Hall of Fame American professional basketball player and coach.
- 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_69d806b40ab4819094adf6c374f4811a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990f6d34c8190ba19dc2df7d42c22 |
completed | April 11, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716e7b9a48190a33b04df8ad45ed8 |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:29 p.m.