Triple
T16408767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krefeld |
E398502
|
entity |
| Predicate | twinTown |
P1072
|
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: [Krefeld, twinTown, Leverkusen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leverkusen Context triple: [Krefeld, twinTown, 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.
Munich
"Munich" is a 2005 historical drama thriller film directed by Steven Spielberg that depicts the covert Israeli response to the 1972 Munich Olympics massacre.
-
D.
Wolfsburg
Wolfsburg is a German city best known as the headquarters and main production site of the Volkswagen automobile company.
-
E.
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.
- 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_69d87f2950248190bc8ad9b9bebdc8c8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32870e44c8190aae7bc6e6022ceb7 |
completed | April 18, 2026, 6:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a003c64a05c8190a59e800ce2318052 |
completed | May 10, 2026, 8:05 a.m. |
Created at: April 10, 2026, 5:09 a.m.