Triple
T14017832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fleesensee |
E337244
|
entity |
| Predicate | hasNearbySettlement |
P4647
|
FINISHED |
| Object | Göhren-Lebbin |
E1073365
|
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: Göhren-Lebbin | Statement: [Fleesensee, hasNearbySettlement, Göhren-Lebbin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Göhren-Lebbin Context triple: [Fleesensee, hasNearbySettlement, Göhren-Lebbin]
-
A.
Göhren-Lebbin
chosen
Göhren-Lebbin is a small resort municipality in the Mecklenburg Lake District of northeastern Germany, known for its tourism, lakeside recreation, and golf and spa facilities.
-
B.
Göhren
Göhren is a seaside resort town on the Baltic Sea coast of Germany, located on the island of Rügen and known for its beaches and tourism.
-
C.
Göhrde
Göhrde is a municipality in Lower Saxony, Germany, known for its extensive forested areas and historical royal hunting grounds.
-
D.
Hasselwerder
Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
-
E.
Hettstedt
Hettstedt is a small German town in the state of Saxony-Anhalt, historically known for its copper mining and metalworking industry.
- 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_69d81c6543a48190bd5ba93d7419e797 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2f3b5b088190a58715779d2c46a6 |
completed | April 14, 2026, 12:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbc32d77108190b038e8a750738439 |
completed | May 6, 2026, 10:39 p.m. |
Created at: April 9, 2026, 10:19 p.m.