Triple
T15597330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Artland Dragons |
E374935
|
entity |
| Predicate | homeCity |
P263
|
FINISHED |
| Object | Quakenbrück |
E14423
|
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: Quakenbrück | Statement: [Artland Dragons, homeCity, Quakenbrück]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Quakenbrück Context triple: [Artland Dragons, homeCity, Quakenbrück]
-
A.
Quakenbrück
chosen
Quakenbrück is a small historic town in Lower Saxony, Germany, known for its medieval architecture and location in the Artland region.
-
B.
Kleeberg
Kleeberg is a Polish surname most notably associated with General Franciszek Kleeberg, a commander in the early stages of World War II.
-
C.
Warthbrücken
Warthbrücken is the German name for the Polish town of Koło, located in central Poland on the Warta River.
-
D.
Bersenbrück
Bersenbrück is a small town in Lower Saxony, Germany, known for its historic abbey and its location on the river Hase.
-
E.
Hochbrück
Hochbrück is a district of the Bavarian town of Garching near Munich, known for its industrial areas and proximity to major research and technology facilities.
- 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_69d85cce25008190b13b52745fbd719b |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e609ab081909feb486a57439960 |
completed | April 16, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff5f355ff48190a2c2c262c09e6de0 |
completed | May 9, 2026, 4:22 p.m. |
Created at: April 10, 2026, 4:12 a.m.