Triple
T8200602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regierungsbezirk Düsseldorf |
E191560
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Leverkusen |
E296756
|
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: Leverkusen | Statement: [Regierungsbezirk Düsseldorf, containsCity, Leverkusen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leverkusen Context triple: [Regierungsbezirk Düsseldorf, containsCity, Leverkusen]
-
A.
Leverkusen
chosen
Leverkusen is a city in western Germany, known for its chemical industry and as the home of the football club Bayer 04 Leverkusen.
-
B.
Munich
Munich is the capital and largest city of the German state of Bavaria, renowned for its rich cultural scene, historic architecture, and the annual Oktoberfest beer festival.
-
C.
Wolfsburg
Wolfsburg is a German city best known as the headquarters and main production site of the Volkswagen automobile company.
-
D.
Ingolstadt
Ingolstadt is a historic city in southern Germany known for its medieval architecture, university tradition, and role as a major hub of the automotive industry.
-
E.
Dortmund
Dortmund is a major city in western Germany known for its rich football culture, industrial heritage, and home club Borussia Dortmund.
- 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_69ca82c6e9548190a4c5ca14516e4417 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb5df565dc819099537fc06b694b40 |
completed | March 31, 2026, 5:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd94c7db688190a755d0143c71c2b2 |
completed | April 1, 2026, 9:57 p.m. |
Created at: March 30, 2026, 5:43 p.m.