Triple
T14017807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fleesensee |
E337244
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Malchow |
E767634
|
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: Malchow | Statement: [Fleesensee, locatedNear, Malchow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malchow Context triple: [Fleesensee, locatedNear, Malchow]
-
A.
Malchow
chosen
Malchow is a small locality within the Berlin borough of Lichtenberg, known for its more rural character and green surroundings compared to the inner city.
-
B.
Melchow
Melchow is a small municipality in the Barnim district of the federal state of Brandenburg in northeastern Germany.
-
C.
Maienwerder
Maienwerder is a small island located in the Tegeler See lake in Berlin, Germany, known for its natural setting and limited accessibility.
-
D.
Borghorst
Borghorst is a district of the German town Steinfurt in North Rhine-Westphalia, known historically for its textile industry and regional cultural heritage.
-
E.
Havixbeck
Havixbeck is a municipality in North Rhine-Westphalia, Germany, known for its rural character and proximity to the city of Münster.
- 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_69fbacad948c81909db7187da5a9b97d |
completed | May 6, 2026, 9:03 p.m. |
Created at: April 9, 2026, 10:19 p.m.